Running ‘testthat.R’ [101s/122s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(rBiasCorrection)
  > 
  > local_edition(3)
  > 
  > test_check("rBiasCorrection")
  [20250402_172054.]: Entered 'clean_dt'-Function
  [20250402_172054.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
  [20250402_172054.]: got experimental data
  [20250402_172054.]: Entered 'clean_dt'-Function
  [20250402_172054.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
  [20250402_172054.]: got calibration data
  [20250402_172054.]: ### Starting with regression calculations ###
  [20250402_172054.]: Entered 'regression_type1'-Function
  [20250402_172054.]: # CpG-site: CpG#1
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172054.]: Logging df_agg: CpG#1
  [20250402_172054.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172054.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_172054.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172054.]: Entered 'hyperbolic_regression'-Function
  [20250402_172054.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172054.]: Entered 'cubic_regression'-Function
  [20250402_172054.]: 'cubic_regression': minmax = FALSE
  [20250402_172054.]: # CpG-site: CpG#2
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172054.]: Logging df_agg: CpG#2
  [20250402_172054.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172054.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_172054.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172054.]: Entered 'hyperbolic_regression'-Function
  [20250402_172054.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172055.]: Entered 'cubic_regression'-Function
  [20250402_172055.]: 'cubic_regression': minmax = FALSE
  [20250402_172055.]: # CpG-site: CpG#3
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172055.]: Logging df_agg: CpG#3
  [20250402_172055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172055.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_172055.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172055.]: Entered 'hyperbolic_regression'-Function
  [20250402_172055.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172055.]: Entered 'cubic_regression'-Function
  [20250402_172055.]: 'cubic_regression': minmax = FALSE
  [20250402_172055.]: # CpG-site: CpG#4
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172055.]: Logging df_agg: CpG#4
  [20250402_172055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172055.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_172055.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172055.]: Entered 'hyperbolic_regression'-Function
  [20250402_172055.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172055.]: Entered 'cubic_regression'-Function
  [20250402_172055.]: 'cubic_regression': minmax = FALSE
  [20250402_172055.]: # CpG-site: CpG#5
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172055.]: Logging df_agg: CpG#5
  [20250402_172055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172055.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_172055.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172055.]: Entered 'hyperbolic_regression'-Function
  [20250402_172055.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172055.]: Entered 'cubic_regression'-Function
  [20250402_172055.]: 'cubic_regression': minmax = FALSE
  [20250402_172055.]: # CpG-site: CpG#6
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172055.]: Logging df_agg: CpG#6
  [20250402_172055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172055.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_172055.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172055.]: Entered 'hyperbolic_regression'-Function
  [20250402_172055.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172055.]: Entered 'cubic_regression'-Function
  [20250402_172055.]: 'cubic_regression': minmax = FALSE
  [20250402_172055.]: # CpG-site: CpG#7
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172055.]: Logging df_agg: CpG#7
  [20250402_172055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172055.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_172055.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172055.]: Entered 'hyperbolic_regression'-Function
  [20250402_172055.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172055.]: Entered 'cubic_regression'-Function
  [20250402_172055.]: 'cubic_regression': minmax = FALSE
  [20250402_172055.]: # CpG-site: CpG#8
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172055.]: Logging df_agg: CpG#8
  [20250402_172055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172055.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_172055.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172055.]: Entered 'hyperbolic_regression'-Function
  [20250402_172055.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172055.]: Entered 'cubic_regression'-Function
  [20250402_172055.]: 'cubic_regression': minmax = FALSE
  [20250402_172055.]: # CpG-site: CpG#9
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172055.]: Logging df_agg: CpG#9
  [20250402_172055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172055.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_172055.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172055.]: Entered 'hyperbolic_regression'-Function
  [20250402_172055.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172056.]: Entered 'cubic_regression'-Function
  [20250402_172056.]: 'cubic_regression': minmax = FALSE
  [20250402_172056.]: # CpG-site: row_means
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172056.]: Logging df_agg: row_means
  [20250402_172056.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172056.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_172056.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172056.]: Entered 'hyperbolic_regression'-Function
  [20250402_172056.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172056.]: Entered 'cubic_regression'-Function
  [20250402_172056.]: 'cubic_regression': minmax = FALSE
  [20250402_172059.]: Entered 'regression_type1'-Function
  [20250402_172059.]: # CpG-site: CpG#1
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172100.]: Logging df_agg: CpG#1
  [20250402_172100.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172100.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_172100.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172100.]: Entered 'hyperbolic_regression'-Function
  [20250402_172100.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172100.]: Entered 'cubic_regression'-Function
  [20250402_172100.]: 'cubic_regression': minmax = FALSE
  [20250402_172100.]: # CpG-site: CpG#2
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172100.]: Logging df_agg: CpG#2
  [20250402_172100.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172100.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_172100.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172100.]: Entered 'hyperbolic_regression'-Function
  [20250402_172100.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172100.]: Entered 'cubic_regression'-Function
  [20250402_172100.]: 'cubic_regression': minmax = FALSE
  [20250402_172100.]: # CpG-site: CpG#3
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172100.]: Logging df_agg: CpG#3
  [20250402_172100.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172100.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_172100.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172100.]: Entered 'hyperbolic_regression'-Function
  [20250402_172100.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172100.]: Entered 'cubic_regression'-Function
  [20250402_172100.]: 'cubic_regression': minmax = FALSE
  [20250402_172100.]: # CpG-site: CpG#4
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172100.]: Logging df_agg: CpG#4
  [20250402_172100.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172100.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_172100.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172100.]: Entered 'hyperbolic_regression'-Function
  [20250402_172100.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172100.]: Entered 'cubic_regression'-Function
  [20250402_172100.]: 'cubic_regression': minmax = FALSE
  [20250402_172100.]: # CpG-site: CpG#5
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172100.]: Logging df_agg: CpG#5
  [20250402_172100.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172100.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_172100.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172100.]: Entered 'hyperbolic_regression'-Function
  [20250402_172100.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172100.]: Entered 'cubic_regression'-Function
  [20250402_172100.]: 'cubic_regression': minmax = FALSE
  [20250402_172100.]: # CpG-site: CpG#6
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172100.]: Logging df_agg: CpG#6
  [20250402_172100.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172100.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_172100.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172100.]: Entered 'hyperbolic_regression'-Function
  [20250402_172100.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172100.]: Entered 'cubic_regression'-Function
  [20250402_172100.]: 'cubic_regression': minmax = FALSE
  [20250402_172100.]: # CpG-site: CpG#7
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172100.]: Logging df_agg: CpG#7
  [20250402_172100.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172100.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_172100.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172100.]: Entered 'hyperbolic_regression'-Function
  [20250402_172100.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172100.]: Entered 'cubic_regression'-Function
  [20250402_172100.]: 'cubic_regression': minmax = FALSE
  [20250402_172100.]: # CpG-site: CpG#8
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172100.]: Logging df_agg: CpG#8
  [20250402_172100.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172100.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_172100.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172100.]: Entered 'hyperbolic_regression'-Function
  [20250402_172100.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172100.]: Entered 'cubic_regression'-Function
  [20250402_172100.]: 'cubic_regression': minmax = FALSE
  [20250402_172100.]: # CpG-site: CpG#9
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172100.]: Logging df_agg: CpG#9
  [20250402_172100.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172100.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_172100.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172100.]: Entered 'hyperbolic_regression'-Function
  [20250402_172100.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172101.]: Entered 'cubic_regression'-Function
  [20250402_172101.]: 'cubic_regression': minmax = FALSE
  [20250402_172101.]: # CpG-site: row_means
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172101.]: Logging df_agg: row_means
  [20250402_172101.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172101.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_172101.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172101.]: Entered 'hyperbolic_regression'-Function
  [20250402_172101.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172101.]: Entered 'cubic_regression'-Function
  [20250402_172101.]: 'cubic_regression': minmax = FALSE
  [20250402_172102.]: Entered 'clean_dt'-Function
  [20250402_172102.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
  [20250402_172102.]: got experimental data
  [20250402_172102.]: Entered 'clean_dt'-Function
  [20250402_172102.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
  [20250402_172102.]: got calibration data
  [20250402_172102.]: ### Starting with regression calculations ###
  [20250402_172102.]: Entered 'regression_type1'-Function
  [20250402_172103.]: # CpG-site: CpG#1
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172103.]: Logging df_agg: CpG#1
  [20250402_172103.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172103.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_172103.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172103.]: Entered 'hyperbolic_regression'-Function
  [20250402_172103.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172103.]: Entered 'cubic_regression'-Function
  [20250402_172103.]: 'cubic_regression': minmax = FALSE
  [20250402_172103.]: # CpG-site: CpG#2
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172103.]: Logging df_agg: CpG#2
  [20250402_172103.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172103.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_172103.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172103.]: Entered 'hyperbolic_regression'-Function
  [20250402_172103.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172103.]: Entered 'cubic_regression'-Function
  [20250402_172103.]: 'cubic_regression': minmax = FALSE
  [20250402_172103.]: # CpG-site: CpG#3
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172103.]: Logging df_agg: CpG#3
  [20250402_172103.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172103.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_172103.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172103.]: Entered 'hyperbolic_regression'-Function
  [20250402_172103.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172104.]: Entered 'cubic_regression'-Function
  [20250402_172104.]: 'cubic_regression': minmax = FALSE
  [20250402_172104.]: # CpG-site: CpG#4
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172104.]: Logging df_agg: CpG#4
  [20250402_172104.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172104.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_172104.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172104.]: Entered 'hyperbolic_regression'-Function
  [20250402_172104.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172104.]: Entered 'cubic_regression'-Function
  [20250402_172104.]: 'cubic_regression': minmax = FALSE
  [20250402_172104.]: # CpG-site: CpG#5
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172104.]: Logging df_agg: CpG#5
  [20250402_172104.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172104.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_172104.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172104.]: Entered 'hyperbolic_regression'-Function
  [20250402_172104.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172104.]: Entered 'cubic_regression'-Function
  [20250402_172104.]: 'cubic_regression': minmax = FALSE
  [20250402_172103.]: # CpG-site: CpG#6
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172103.]: Logging df_agg: CpG#6
  [20250402_172103.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172103.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_172103.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172103.]: Entered 'hyperbolic_regression'-Function
  [20250402_172103.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172104.]: Entered 'cubic_regression'-Function
  [20250402_172104.]: 'cubic_regression': minmax = FALSE
  [20250402_172104.]: # CpG-site: CpG#7
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172104.]: Logging df_agg: CpG#7
  [20250402_172104.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172104.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_172104.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172104.]: Entered 'hyperbolic_regression'-Function
  [20250402_172104.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172104.]: Entered 'cubic_regression'-Function
  [20250402_172104.]: 'cubic_regression': minmax = FALSE
  [20250402_172104.]: # CpG-site: CpG#8
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172104.]: Logging df_agg: CpG#8
  [20250402_172104.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172104.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_172104.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172104.]: Entered 'hyperbolic_regression'-Function
  [20250402_172104.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172104.]: Entered 'cubic_regression'-Function
  [20250402_172104.]: 'cubic_regression': minmax = FALSE
  [20250402_172104.]: # CpG-site: CpG#9
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172104.]: Logging df_agg: CpG#9
  [20250402_172104.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172104.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_172104.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172104.]: Entered 'hyperbolic_regression'-Function
  [20250402_172104.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172104.]: Entered 'cubic_regression'-Function
  [20250402_172104.]: 'cubic_regression': minmax = FALSE
  [20250402_172104.]: # CpG-site: row_means
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172104.]: Logging df_agg: row_means
  [20250402_172104.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172104.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_172104.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172104.]: Entered 'hyperbolic_regression'-Function
  [20250402_172104.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172104.]: Entered 'cubic_regression'-Function
  [20250402_172104.]: 'cubic_regression': minmax = FALSE
  [20250402_172106.]: Entered 'regression_type1'-Function
  [20250402_172107.]: # CpG-site: CpG#1
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172107.]: Logging df_agg: CpG#1
  [20250402_172107.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172107.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_172107.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172107.]: Entered 'hyperbolic_regression'-Function
  [20250402_172107.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172108.]: Entered 'cubic_regression'-Function
  [20250402_172108.]: 'cubic_regression': minmax = FALSE
  [20250402_172108.]: # CpG-site: CpG#2
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172108.]: Logging df_agg: CpG#2
  [20250402_172108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172108.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_172108.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172108.]: Entered 'hyperbolic_regression'-Function
  [20250402_172108.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172108.]: Entered 'cubic_regression'-Function
  [20250402_172108.]: 'cubic_regression': minmax = FALSE
  [20250402_172108.]: # CpG-site: CpG#3
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172108.]: Logging df_agg: CpG#3
  [20250402_172108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172108.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_172108.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172108.]: Entered 'hyperbolic_regression'-Function
  [20250402_172108.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172108.]: Entered 'cubic_regression'-Function
  [20250402_172108.]: 'cubic_regression': minmax = FALSE
  [20250402_172108.]: # CpG-site: CpG#4
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172108.]: Logging df_agg: CpG#4
  [20250402_172108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172108.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_172108.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172108.]: Entered 'hyperbolic_regression'-Function
  [20250402_172108.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172108.]: Entered 'cubic_regression'-Function
  [20250402_172108.]: 'cubic_regression': minmax = FALSE
  [20250402_172108.]: # CpG-site: CpG#5
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172108.]: Logging df_agg: CpG#5
  [20250402_172108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172108.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_172108.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172108.]: Entered 'hyperbolic_regression'-Function
  [20250402_172108.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172108.]: Entered 'cubic_regression'-Function
  [20250402_172108.]: 'cubic_regression': minmax = FALSE
  [20250402_172108.]: # CpG-site: CpG#6
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172108.]: Logging df_agg: CpG#6
  [20250402_172108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172108.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_172108.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172108.]: Entered 'hyperbolic_regression'-Function
  [20250402_172108.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172108.]: Entered 'cubic_regression'-Function
  [20250402_172108.]: 'cubic_regression': minmax = FALSE
  [20250402_172108.]: # CpG-site: CpG#7
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172108.]: Logging df_agg: CpG#7
  [20250402_172108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172108.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_172108.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172108.]: Entered 'hyperbolic_regression'-Function
  [20250402_172108.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172108.]: Entered 'cubic_regression'-Function
  [20250402_172108.]: 'cubic_regression': minmax = FALSE
  [20250402_172108.]: # CpG-site: CpG#8
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172108.]: Logging df_agg: CpG#8
  [20250402_172108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172108.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_172108.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172108.]: Entered 'hyperbolic_regression'-Function
  [20250402_172108.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172108.]: Entered 'cubic_regression'-Function
  [20250402_172108.]: 'cubic_regression': minmax = FALSE
  [20250402_172108.]: # CpG-site: CpG#9
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172108.]: Logging df_agg: CpG#9
  [20250402_172108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172108.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_172108.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172108.]: Entered 'hyperbolic_regression'-Function
  [20250402_172108.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172109.]: Entered 'cubic_regression'-Function
  [20250402_172109.]: 'cubic_regression': minmax = FALSE
  [20250402_172109.]: # CpG-site: row_means
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172109.]: Logging df_agg: row_means
  [20250402_172109.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172109.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_172109.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172109.]: Entered 'hyperbolic_regression'-Function
  [20250402_172109.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172109.]: Entered 'cubic_regression'-Function
  [20250402_172109.]: 'cubic_regression': minmax = FALSE
  [20250402_172110.]: Entered 'solving_equations'-Function
  [20250402_172110.]: Solving hyperbolic regression for CpG#1
  Hyperbolic solved: -2.23222990163966
  [20250402_172110.]: Samplename: 0  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -2.232  
  --> '-10 < root < 0' --> substitute 0
  Hyperbolic solved: 12.1698489850618
  [20250402_172110.]: Samplename: 12.5  
  Root: 12.17  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 24.4781920312644
  [20250402_172110.]: Samplename: 25  
  Root: 24.478  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 38.173044740918
  [20250402_172110.]: Samplename: 37.5  
  Root: 38.173  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 52.3349371964438
  [20250402_172110.]: Samplename: 50  
  Root: 52.335  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 65.4582773627666
  [20250402_172110.]: Samplename: 62.5  
  Root: 65.458  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 75.0090795260796
  [20250402_172110.]: Samplename: 75  
  Root: 75.009  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 81.5271920968417
  [20250402_172110.]: Samplename: 87.5  
  Root: 81.527  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 102.400893095062
  [20250402_172110.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 102.401  
  --> '100 < root < 110' --> substitute 100
  [20250402_172110.]: Solving hyperbolic regression for CpG#2
  Hyperbolic solved: 1.13660501904968
  [20250402_172110.]: Samplename: 0  
  Root: 1.137  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 11.4129696733689
  [20250402_172110.]: Samplename: 12.5  
  Root: 11.413  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.174000526428
  [20250402_172110.]: Samplename: 25  
  Root: 26.174  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 35.1050449117028
  [20250402_172110.]: Samplename: 37.5  
  Root: 35.105  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 47.685500330611
  [20250402_172110.]: Samplename: 50  
  Root: 47.686  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 67.1440494417104
  [20250402_172110.]: Samplename: 62.5  
  Root: 67.144  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 75.7644668894086
  [20250402_172110.]: Samplename: 75  
  Root: 75.764  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 84.4054158616395
  [20250402_172110.]: Samplename: 87.5  
  Root: 84.405  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100.94827248399
  [20250402_172110.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 100.948  
  --> '100 < root < 110' --> substitute 100
  [20250402_172110.]: Solving hyperbolic regression for CpG#3
  Hyperbolic solved: 0.51235653688495
  [20250402_172110.]: Samplename: 0  
  Root: 0.512  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 10.7523884294604
  [20250402_172110.]: Samplename: 12.5  
  Root: 10.752  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 25.5218907947761
  [20250402_172110.]: Samplename: 25  
  Root: 25.522  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 36.5270462675211
  [20250402_172110.]: Samplename: 37.5  
  Root: 36.527  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 50.7909245028224
  [20250402_172110.]: Samplename: 50  
  Root: 50.791  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 64.8686317550184
  [20250402_172110.]: Samplename: 62.5  
  Root: 64.869  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 77.5524188495235
  [20250402_172110.]: Samplename: 75  
  Root: 77.552  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 80.4374617358174
  [20250402_172110.]: Samplename: 87.5  
  Root: 80.437  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 102.704024900825
  [20250402_172110.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 102.704  
  --> '100 < root < 110' --> substitute 100
  [20250402_172110.]: Solving hyperbolic regression for CpG#4
  Hyperbolic solved: -0.519503092357606
  [20250402_172110.]: Samplename: 0  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -0.52  
  --> '-10 < root < 0' --> substitute 0
  Hyperbolic solved: 12.4934147844872
  [20250402_172110.]: Samplename: 12.5  
  Root: 12.493  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 24.2685420024115
  [20250402_172110.]: Samplename: 25  
  Root: 24.269  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 38.0817128465023
  [20250402_172110.]: Samplename: 37.5  
  Root: 38.082  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 48.5843181174811
  [20250402_172110.]: Samplename: 50  
  Root: 48.584  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 67.6722399183037
  [20250402_172110.]: Samplename: 62.5  
  Root: 67.672  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 74.1549277799119
  [20250402_172110.]: Samplename: 75  
  Root: 74.155  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 82.8821797890026
  [20250402_172110.]: Samplename: 87.5  
  Root: 82.882  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 102.0791269023
  [20250402_172110.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 102.079  
  --> '100 < root < 110' --> substitute 100
  [20250402_172110.]: Solving hyperbolic regression for CpG#5
  Hyperbolic solved: 2.41558626275183
  [20250402_172110.]: Samplename: 0  
  Root: 2.416  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 10.1649674907454
  [20250402_172110.]: Samplename: 12.5  
  Root: 10.165  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 23.9830820412762
  [20250402_172110.]: Samplename: 25  
  Root: 23.983  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 37.2773619900429
  [20250402_172110.]: Samplename: 37.5  
  Root: 37.277  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 50.8659386543864
  [20250402_172110.]: Samplename: 50  
  Root: 50.866  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 62.4342273571069
  [20250402_172110.]: Samplename: 62.5  
  Root: 62.434  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 76.3915260534323
  [20250402_172110.]: Samplename: 75  
  Root: 76.392  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 86.159788778566
  [20250402_172110.]: Samplename: 87.5  
  Root: 86.16  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100.267759893323
  [20250402_172110.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 100.268  
  --> '100 < root < 110' --> substitute 100
  [20250402_172110.]: Solving hyperbolic regression for CpG#6
  Hyperbolic solved: 0.138163748613034
  [20250402_172110.]: Samplename: 0  
  Root: 0.138  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 11.8635558881981
  [20250402_172110.]: Samplename: 12.5  
  Root: 11.864  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.5107449550797
  [20250402_172110.]: Samplename: 25  
  Root: 26.511  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 35.3205073050661
  [20250402_172110.]: Samplename: 37.5  
  Root: 35.321  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 50.0570767570666
  [20250402_172110.]: Samplename: 50  
  Root: 50.057  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 64.9602944381018
  [20250402_172110.]: Samplename: 62.5  
  Root: 64.96  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 73.66890571617
  [20250402_172110.]: Samplename: 75  
  Root: 73.669  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 87.1266086585036
  [20250402_172110.]: Samplename: 87.5  
  Root: 87.127  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100.261637014212
  [20250402_172110.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 100.262  
  --> '100 < root < 110' --> substitute 100
  [20250402_172110.]: Solving hyperbolic regression for CpG#7
  Hyperbolic solved: -1.37238087287012
  [20250402_172110.]: Samplename: 0  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -1.372  
  --> '-10 < root < 0' --> substitute 0
  Hyperbolic solved: 10.1993162352498
  [20250402_172110.]: Samplename: 12.5  
  Root: 10.199  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 24.595178967123
  [20250402_172110.]: Samplename: 25  
  Root: 24.595  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 37.8310421041787
  [20250402_172110.]: Samplename: 37.5  
  Root: 37.831  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 53.5588739724067
  [20250402_172110.]: Samplename: 50  
  Root: 53.559  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 65.9364947980258
  [20250402_172110.]: Samplename: 62.5  
  Root: 65.936  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 75.7361094434913
  [20250402_172110.]: Samplename: 75  
  Root: 75.736  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 79.432823759854
  [20250402_172110.]: Samplename: 87.5  
  Root: 79.433  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 103.004237013737
  [20250402_172110.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 103.004  
  --> '100 < root < 110' --> substitute 100
  [20250402_172110.]: Solving hyperbolic regression for CpG#8
  Hyperbolic solved: 2.80068218205093
  [20250402_172110.]: Samplename: 0  
  Root: 2.801  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 9.27535134596596
  [20250402_172110.]: Samplename: 12.5  
  Root: 9.275  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 25.4762621928197
  [20250402_172110.]: Samplename: 25  
  Root: 25.476  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 34.0122075735416
  [20250402_172110.]: Samplename: 37.5  
  Root: 34.012  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 51.7842655662325
  [20250402_172110.]: Samplename: 50  
  Root: 51.784  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 64.6732311906145
  [20250402_172110.]: Samplename: 62.5  
  Root: 64.673  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 78.4326978859189
  [20250402_172110.]: Samplename: 75  
  Root: 78.433  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 81.3427232852719
  [20250402_172110.]: Samplename: 87.5  
  Root: 81.343  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 101.964406640583
  [20250402_172110.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 101.964  
  --> '100 < root < 110' --> substitute 100
  [20250402_172110.]: Solving hyperbolic regression for CpG#9
  Hyperbolic solved: -2.13403721845678
  [20250402_172110.]: Samplename: 0  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -2.134  
  --> '-10 < root < 0' --> substitute 0
  Hyperbolic solved: 10.5082192457956
  [20250402_172110.]: Samplename: 12.5  
  Root: 10.508  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.9164567253388
  [20250402_172110.]: Samplename: 25  
  Root: 26.916  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 36.8334779159501
  [20250402_172110.]: Samplename: 37.5  
  Root: 36.833  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 52.0097895977263
  [20250402_172110.]: Samplename: 50  
  Root: 52.01  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 64.8930527921581
  [20250402_172110.]: Samplename: 62.5  
  Root: 64.893  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 74.5671055499357
  [20250402_172110.]: Samplename: 75  
  Root: 74.567  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 84.5294954832669
  [20250402_172110.]: Samplename: 87.5  
  Root: 84.529  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 101.047146466811
  [20250402_172110.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 101.047  
  --> '100 < root < 110' --> substitute 100
  [20250402_172110.]: Solving hyperbolic regression for row_means
  Hyperbolic solved: 0.290941088603071
  [20250402_172110.]: Samplename: 0  
  Root: 0.291  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 11.0412408065783
  [20250402_172110.]: Samplename: 12.5  
  Root: 11.041  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 25.4081501047696
  [20250402_172110.]: Samplename: 25  
  Root: 25.408  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 36.5243719024532
  [20250402_172110.]: Samplename: 37.5  
  Root: 36.524  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 50.7348824329668
  [20250402_172110.]: Samplename: 50  
  Root: 50.735  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 65.3135209766198
  [20250402_172110.]: Samplename: 62.5  
  Root: 65.314  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 75.5342709041132
  [20250402_172110.]: Samplename: 75  
  Root: 75.534  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 83.2411228425212
  [20250402_172110.]: Samplename: 87.5  
  Root: 83.241  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 101.666942781592
  [20250402_172110.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 101.667  
  --> '100 < root < 110' --> substitute 100
  [20250402_172110.]: ### Starting with regression calculations ###
  [20250402_172110.]: Entered 'regression_type1'-Function
  [20250402_172111.]: # CpG-site: CpG#1
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
  [20250402_172111.]: Logging df_agg: CpG#1
  [20250402_172111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172111.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
  [20250402_172111.]: Entered 'hyperbolic_regression'-Function
  [20250402_172111.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172111.]: Entered 'cubic_regression'-Function
  [20250402_172111.]: 'cubic_regression': minmax = FALSE
  [20250402_172111.]: # CpG-site: CpG#2
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
  [20250402_172111.]: Logging df_agg: CpG#2
  [20250402_172111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172111.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
  [20250402_172111.]: Entered 'hyperbolic_regression'-Function
  [20250402_172111.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172111.]: Entered 'cubic_regression'-Function
  [20250402_172111.]: 'cubic_regression': minmax = FALSE
  [20250402_172111.]: # CpG-site: CpG#3
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
  [20250402_172111.]: Logging df_agg: CpG#3
  [20250402_172111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172111.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
  [20250402_172111.]: Entered 'hyperbolic_regression'-Function
  [20250402_172111.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172111.]: Entered 'cubic_regression'-Function
  [20250402_172111.]: 'cubic_regression': minmax = FALSE
  [20250402_172111.]: # CpG-site: CpG#4
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
  [20250402_172111.]: Logging df_agg: CpG#4
  [20250402_172111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172111.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
  [20250402_172111.]: Entered 'hyperbolic_regression'-Function
  [20250402_172111.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172112.]: Entered 'cubic_regression'-Function
  [20250402_172112.]: 'cubic_regression': minmax = FALSE
  [20250402_172112.]: # CpG-site: CpG#5
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
  [20250402_172112.]: Logging df_agg: CpG#5
  [20250402_172112.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172112.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
  [20250402_172112.]: Entered 'hyperbolic_regression'-Function
  [20250402_172112.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172112.]: Entered 'cubic_regression'-Function
  [20250402_172112.]: 'cubic_regression': minmax = FALSE
  [20250402_172111.]: # CpG-site: CpG#6
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
  [20250402_172111.]: Logging df_agg: CpG#6
  [20250402_172111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172111.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
  [20250402_172111.]: Entered 'hyperbolic_regression'-Function
  [20250402_172111.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172111.]: Entered 'cubic_regression'-Function
  [20250402_172111.]: 'cubic_regression': minmax = FALSE
  [20250402_172111.]: # CpG-site: CpG#7
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
  [20250402_172111.]: Logging df_agg: CpG#7
  [20250402_172111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172111.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
  [20250402_172111.]: Entered 'hyperbolic_regression'-Function
  [20250402_172111.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172112.]: Entered 'cubic_regression'-Function
  [20250402_172112.]: 'cubic_regression': minmax = FALSE
  [20250402_172112.]: # CpG-site: CpG#8
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
  [20250402_172112.]: Logging df_agg: CpG#8
  [20250402_172112.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172112.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
  [20250402_172112.]: Entered 'hyperbolic_regression'-Function
  [20250402_172112.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172112.]: Entered 'cubic_regression'-Function
  [20250402_172112.]: 'cubic_regression': minmax = FALSE
  [20250402_172112.]: # CpG-site: CpG#9
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
  [20250402_172112.]: Logging df_agg: CpG#9
  [20250402_172112.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172112.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
  [20250402_172112.]: Entered 'hyperbolic_regression'-Function
  [20250402_172112.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172112.]: Entered 'cubic_regression'-Function
  [20250402_172112.]: 'cubic_regression': minmax = FALSE
  [20250402_172112.]: # CpG-site: row_means
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
  [20250402_172112.]: Logging df_agg: row_means
  [20250402_172112.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172112.]: c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
  [20250402_172112.]: Entered 'hyperbolic_regression'-Function
  [20250402_172112.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172112.]: Entered 'cubic_regression'-Function
  [20250402_172112.]: 'cubic_regression': minmax = FALSE
  [20250402_172113.]: Entered 'solving_equations'-Function
  [20250402_172113.]: Solving cubic regression for CpG#1
  Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
  [20250402_172113.]: Samplename: 0  
  Root: 1.334  
  --> Root in between the borders! Added to results.
  Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
  [20250402_172113.]: Samplename: 12.5  
  Root: 11.446  
  --> Root in between the borders! Added to results.
  Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
  [20250402_172113.]: Samplename: 25  
  Root: 22.228  
  --> Root in between the borders! Added to results.
  Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
  [20250402_172113.]: Samplename: 37.5  
  Root: 36.374  
  --> Root in between the borders! Added to results.
  Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
  [20250402_172113.]: Samplename: 50  
  Root: 52.044  
  --> Root in between the borders! Added to results.
  Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
  [20250402_172113.]: Samplename: 62.5  
  Root: 66.144  
  --> Root in between the borders! Added to results.
  Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
  [20250402_172113.]: Samplename: 75  
  Root: 75.864  
  --> Root in between the borders! Added to results.
  Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
  [20250402_172113.]: Samplename: 87.5  
  Root: 82.254  
  --> Root in between the borders! Added to results.
  Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
  [20250402_172113.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 101.877  
  --> '100 < root < 110' --> substitute 100
  [20250402_172113.]: Solving cubic regression for CpG#2
  Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172113.]: Samplename: 0  
  Root: 0.549  
  --> Root in between the borders! Added to results.
  Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172113.]: Samplename: 12.5  
  Root: 11.533  
  --> Root in between the borders! Added to results.
  Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172113.]: Samplename: 25  
  Root: 26.628  
  --> Root in between the borders! Added to results.
  Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172113.]: Samplename: 37.5  
  Root: 35.509  
  --> Root in between the borders! Added to results.
  Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172113.]: Samplename: 50  
  Root: 47.851  
  --> Root in between the borders! Added to results.
  Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172113.]: Samplename: 62.5  
  Root: 66.893  
  --> Root in between the borders! Added to results.
  Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172113.]: Samplename: 75  
  Root: 75.431  
  --> Root in between the borders! Added to results.
  Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172113.]: Samplename: 87.5  
  Root: 84.118  
  --> Root in between the borders! Added to results.
  Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172113.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 101.287  
  --> '100 < root < 110' --> substitute 100
  [20250402_172113.]: Solving cubic regression for CpG#3
  Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
  [20250402_172113.]: Samplename: 0  
  Root: 1.441  
  --> Root in between the borders! Added to results.
  Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
  [20250402_172113.]: Samplename: 12.5  
  Root: 10.568  
  --> Root in between the borders! Added to results.
  Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
  [20250402_172113.]: Samplename: 25  
  Root: 24.796  
  --> Root in between the borders! Added to results.
  Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
  [20250402_172113.]: Samplename: 37.5  
  Root: 35.952  
  --> Root in between the borders! Added to results.
  Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
  [20250402_172113.]: Samplename: 50  
  Root: 50.684  
  --> Root in between the borders! Added to results.
  Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
  [20250402_172113.]: Samplename: 62.5  
  Root: 65.142  
  --> Root in between the borders! Added to results.
  Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
  [20250402_172113.]: Samplename: 75  
  Root: 77.905  
  --> Root in between the borders! Added to results.
  Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
  [20250402_172113.]: Samplename: 87.5  
  Root: 80.767  
  --> Root in between the borders! Added to results.
  Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
  [20250402_172113.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 102.38  
  --> '100 < root < 110' --> substitute 100
  [20250402_172113.]: Solving cubic regression for CpG#4
  Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
  [20250402_172113.]: Samplename: 0  
  Root: 0.858  
  --> Root in between the borders! Added to results.
  Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
  [20250402_172113.]: Samplename: 12.5  
  Root: 12.086  
  --> Root in between the borders! Added to results.
  Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
  [20250402_172113.]: Samplename: 25  
  Root: 23.316  
  --> Root in between the borders! Added to results.
  Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
  [20250402_172113.]: Samplename: 37.5  
  Root: 37.383  
  --> Root in between the borders! Added to results.
  Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
  [20250402_172113.]: Samplename: 50  
  Root: 48.353  
  --> Root in between the borders! Added to results.
  Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
  [20250402_172113.]: Samplename: 62.5  
  Root: 68.082  
  --> Root in between the borders! Added to results.
  Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
  [20250402_172113.]: Samplename: 75  
  Root: 74.615  
  --> Root in between the borders! Added to results.
  Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
  [20250402_172113.]: Samplename: 87.5  
  Root: 83.254  
  --> Root in between the borders! Added to results.
  Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
  [20250402_172113.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 101.715  
  --> '100 < root < 110' --> substitute 100
  [20250402_172113.]: Solving cubic regression for CpG#5
  Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172113.]: Samplename: 0  
  Root: 1.458  
  --> Root in between the borders! Added to results.
  Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172113.]: Samplename: 12.5  
  Root: 10.347  
  --> Root in between the borders! Added to results.
  Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172113.]: Samplename: 25  
  Root: 24.815  
  --> Root in between the borders! Added to results.
  Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172113.]: Samplename: 37.5  
  Root: 37.902  
  --> Root in between the borders! Added to results.
  Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172113.]: Samplename: 50  
  Root: 50.977  
  --> Root in between the borders! Added to results.
  Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172113.]: Samplename: 62.5  
  Root: 62.126  
  --> Root in between the borders! Added to results.
  Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172113.]: Samplename: 75  
  Root: 75.852  
  --> Root in between the borders! Added to results.
  Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172113.]: Samplename: 87.5  
  Root: 85.767  
  --> Root in between the borders! Added to results.
  Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172113.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 100.743  
  --> '100 < root < 110' --> substitute 100
  [20250402_172113.]: Solving cubic regression for CpG#6
  Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
  [20250402_172113.]: Samplename: 0  
  Root: 0.349  
  --> Root in between the borders! Added to results.
  Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
  [20250402_172113.]: Samplename: 12.5  
  Root: 11.718  
  --> Root in between the borders! Added to results.
  Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
  [20250402_172113.]: Samplename: 25  
  Root: 26.396  
  --> Root in between the borders! Added to results.
  Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
  [20250402_172113.]: Samplename: 37.5  
  Root: 35.301  
  --> Root in between the borders! Added to results.
  Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
  [20250402_172113.]: Samplename: 50  
  Root: 50.134  
  --> Root in between the borders! Added to results.
  Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
  [20250402_172113.]: Samplename: 62.5  
  Root: 64.993  
  --> Root in between the borders! Added to results.
  Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
  [20250402_172113.]: Samplename: 75  
  Root: 73.639  
  --> Root in between the borders! Added to results.
  Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
  [20250402_172113.]: Samplename: 87.5  
  Root: 87.043  
  --> Root in between the borders! Added to results.
  Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
  [20250402_172113.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 100.329  
  --> '100 < root < 110' --> substitute 100
  [20250402_172113.]: Solving cubic regression for CpG#7
  Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
  [20250402_172113.]: Samplename: 0  
  Root: 2.13  
  --> Root in between the borders! Added to results.
  Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
  [20250402_172113.]: Samplename: 12.5  
  Root: 9.973  
  --> Root in between the borders! Added to results.
  Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
  [20250402_172113.]: Samplename: 25  
  Root: 22.206  
  --> Root in between the borders! Added to results.
  Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
  [20250402_172113.]: Samplename: 37.5  
  Root: 35.814  
  --> Root in between the borders! Added to results.
  Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
  [20250402_172113.]: Samplename: 50  
  Root: 53.28  
  --> Root in between the borders! Added to results.
  Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
  [20250402_172113.]: Samplename: 62.5  
  Root: 66.598  
  --> Root in between the borders! Added to results.
  Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
  [20250402_172113.]: Samplename: 75  
  Root: 76.575  
  --> Root in between the borders! Added to results.
  Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
  [20250402_172113.]: Samplename: 87.5  
  Root: 80.219  
  --> Root in between the borders! Added to results.
  Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
  [20250402_172113.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 102.506  
  --> '100 < root < 110' --> substitute 100
  [20250402_172113.]: Solving cubic regression for CpG#8
  Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172113.]: Samplename: 0  
  Root: 2.016  
  --> Root in between the borders! Added to results.
  Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172113.]: Samplename: 12.5  
  Root: 9.458  
  --> Root in between the borders! Added to results.
  Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172113.]: Samplename: 25  
  Root: 26.35  
  --> Root in between the borders! Added to results.
  Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172113.]: Samplename: 37.5  
  Root: 34.728  
  --> Root in between the borders! Added to results.
  Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172113.]: Samplename: 50  
  Root: 51.781  
  --> Root in between the borders! Added to results.
  Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172113.]: Samplename: 62.5  
  Root: 64.192  
  --> Root in between the borders! Added to results.
  Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172113.]: Samplename: 75  
  Root: 77.804  
  --> Root in between the borders! Added to results.
  Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172113.]: Samplename: 87.5  
  Root: 80.758  
  --> Root in between the borders! Added to results.
  Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172113.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 102.834  
  --> '100 < root < 110' --> substitute 100
  [20250402_172113.]: Solving cubic regression for CpG#9
  Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172113.]: Samplename: 0  
  Root: 1.475  
  --> Root in between the borders! Added to results.
  Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172113.]: Samplename: 12.5  
  Root: 10.12  
  --> Root in between the borders! Added to results.
  Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172113.]: Samplename: 25  
  Root: 24.844  
  --> Root in between the borders! Added to results.
  Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172113.]: Samplename: 37.5  
  Root: 35.327  
  --> Root in between the borders! Added to results.
  Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172113.]: Samplename: 50  
  Root: 51.855  
  --> Root in between the borders! Added to results.
  Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172113.]: Samplename: 62.5  
  Root: 65.265  
  --> Root in between the borders! Added to results.
  Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172113.]: Samplename: 75  
  Root: 74.915  
  --> Root in between the borders! Added to results.
  Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172113.]: Samplename: 87.5  
  Root: 84.67  
  --> Root in between the borders! Added to results.
  Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172113.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 101.082  
  --> '100 < root < 110' --> substitute 100
  [20250402_172113.]: Solving cubic regression for row_means
  Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
  [20250402_172113.]: Samplename: 0  
  Root: 1.287  
  --> Root in between the borders! Added to results.
  Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
  [20250402_172113.]: Samplename: 12.5  
  Root: 10.847  
  --> Root in between the borders! Added to results.
  Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
  [20250402_172113.]: Samplename: 25  
  Root: 24.737  
  --> Root in between the borders! Added to results.
  Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
  [20250402_172113.]: Samplename: 37.5  
  Root: 36.02  
  --> Root in between the borders! Added to results.
  Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
  [20250402_172113.]: Samplename: 50  
  Root: 50.639  
  --> Root in between the borders! Added to results.
  Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
  [20250402_172113.]: Samplename: 62.5  
  Root: 65.497  
  --> Root in between the borders! Added to results.
  Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
  [20250402_172113.]: Samplename: 75  
  Root: 75.751  
  --> Root in between the borders! Added to results.
  Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
  [20250402_172113.]: Samplename: 87.5  
  Root: 83.403  
  --> Root in between the borders! Added to results.
  Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
  [20250402_172113.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 101.573  
  --> '100 < root < 110' --> substitute 100
  [20250402_172113.]: ### Starting with regression calculations ###
  [20250402_172113.]: Entered 'regression_type1'-Function
  [20250402_172114.]: # CpG-site: CpG#1
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
  [20250402_172114.]: Logging df_agg: CpG#1
  [20250402_172114.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172114.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
  [20250402_172114.]: Entered 'hyperbolic_regression'-Function
  [20250402_172114.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172114.]: Entered 'cubic_regression'-Function
  [20250402_172114.]: 'cubic_regression': minmax = FALSE
  [20250402_172114.]: # CpG-site: CpG#2
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
  [20250402_172114.]: Logging df_agg: CpG#2
  [20250402_172114.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172114.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
  [20250402_172114.]: Entered 'hyperbolic_regression'-Function
  [20250402_172114.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172115.]: Entered 'cubic_regression'-Function
  [20250402_172115.]: 'cubic_regression': minmax = FALSE
  [20250402_172115.]: # CpG-site: CpG#3
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
  [20250402_172115.]: Logging df_agg: CpG#3
  [20250402_172115.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172115.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
  [20250402_172115.]: Entered 'hyperbolic_regression'-Function
  [20250402_172115.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172115.]: Entered 'cubic_regression'-Function
  [20250402_172115.]: 'cubic_regression': minmax = FALSE
  [20250402_172115.]: # CpG-site: CpG#4
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
  [20250402_172115.]: Logging df_agg: CpG#4
  [20250402_172115.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172115.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
  [20250402_172115.]: Entered 'hyperbolic_regression'-Function
  [20250402_172115.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172115.]: Entered 'cubic_regression'-Function
  [20250402_172115.]: 'cubic_regression': minmax = FALSE
  [20250402_172115.]: # CpG-site: CpG#5
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
  [20250402_172115.]: Logging df_agg: CpG#5
  [20250402_172115.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172115.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
  [20250402_172115.]: Entered 'hyperbolic_regression'-Function
  [20250402_172115.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172115.]: Entered 'cubic_regression'-Function
  [20250402_172115.]: 'cubic_regression': minmax = FALSE
  [20250402_172114.]: # CpG-site: CpG#6
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
  [20250402_172114.]: Logging df_agg: CpG#6
  [20250402_172114.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172114.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
  [20250402_172114.]: Entered 'hyperbolic_regression'-Function
  [20250402_172114.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172115.]: Entered 'cubic_regression'-Function
  [20250402_172115.]: 'cubic_regression': minmax = FALSE
  [20250402_172115.]: # CpG-site: CpG#7
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
  [20250402_172115.]: Logging df_agg: CpG#7
  [20250402_172115.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172115.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
  [20250402_172115.]: Entered 'hyperbolic_regression'-Function
  [20250402_172115.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172115.]: Entered 'cubic_regression'-Function
  [20250402_172115.]: 'cubic_regression': minmax = FALSE
  [20250402_172115.]: # CpG-site: CpG#8
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
  [20250402_172115.]: Logging df_agg: CpG#8
  [20250402_172115.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172115.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
  [20250402_172115.]: Entered 'hyperbolic_regression'-Function
  [20250402_172115.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172115.]: Entered 'cubic_regression'-Function
  [20250402_172115.]: 'cubic_regression': minmax = FALSE
  [20250402_172115.]: # CpG-site: CpG#9
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
  [20250402_172115.]: Logging df_agg: CpG#9
  [20250402_172115.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172115.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
  [20250402_172115.]: Entered 'hyperbolic_regression'-Function
  [20250402_172115.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172115.]: Entered 'cubic_regression'-Function
  [20250402_172115.]: 'cubic_regression': minmax = FALSE
  [20250402_172115.]: # CpG-site: row_means
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
  [20250402_172115.]: Logging df_agg: row_means
  [20250402_172115.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172115.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
  [20250402_172115.]: Entered 'hyperbolic_regression'-Function
  [20250402_172115.]: 'hyperbolic_regression': minmax = FALSE
  [20250402_172116.]: Entered 'cubic_regression'-Function
  [20250402_172116.]: 'cubic_regression': minmax = FALSE
  [20250402_172116.]: Entered 'solving_equations'-Function
  [20250402_172116.]: Solving hyperbolic regression for CpG#1
  Hyperbolic solved: 79.8673456895745
  [20250402_172116.]: Samplename: Sample#1  
  Root: 79.867  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 29.7900184340805
  [20250402_172116.]: Samplename: Sample#10  
  Root: 29.79  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 41.6525415639691
  [20250402_172116.]: Samplename: Sample#2  
  Root: 41.653  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 57.4652090254513
  [20250402_172116.]: Samplename: Sample#3  
  Root: 57.465  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 9.2007130627765
  [20250402_172116.]: Samplename: Sample#4  
  Root: 9.201  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 21.8059600538131
  [20250402_172116.]: Samplename: Sample#5  
  Root: 21.806  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 23.083796735881
  [20250402_172116.]: Samplename: Sample#6  
  Root: 23.084  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 45.5034245569385
  [20250402_172116.]: Samplename: Sample#7  
  Root: 45.503  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 85.6987904075704
  [20250402_172116.]: Samplename: Sample#8  
  Root: 85.699  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: -3.66512807265101
  [20250402_172116.]: Samplename: Sample#9  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -3.665  
  --> '-10 < root < 0' --> substitute 0
  [20250402_172116.]: Solving cubic regression for CpG#2
  Coefficients: -60.0166632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172116.]: Samplename: Sample#1  
  Root: 76.388  
  --> Root in between the borders! Added to results.
  Coefficients: -19.33132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172116.]: Samplename: Sample#10  
  Root: 31.437  
  --> Root in between the borders! Added to results.
  Coefficients: -28.1616632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172116.]: Samplename: Sample#2  
  Root: 42.956  
  --> Root in between the borders! Added to results.
  Coefficients: -42.07832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172116.]: Samplename: Sample#3  
  Root: 58.838  
  --> Root in between the borders! Added to results.
  Coefficients: -2.49332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172117.]: Samplename: Sample#4  
  Root: 4.715  
  --> Root in between the borders! Added to results.
  Coefficients: -11.94832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172117.]: Samplename: Sample#5  
  Root: 20.644  
  --> Root in between the borders! Added to results.
  Coefficients: -10.36332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172117.]: Samplename: Sample#6  
  Root: 18.159  
  --> Root in between the borders! Added to results.
  Coefficients: -26.77132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172117.]: Samplename: Sample#7  
  Root: 41.228  
  --> Root in between the borders! Added to results.
  Coefficients: -70.81532996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172117.]: Samplename: Sample#8  
  Root: 85.785  
  --> Root in between the borders! Added to results.
  Coefficients: -1.41332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172117.]: Samplename: Sample#9  
  Root: 2.703  
  --> Root in between the borders! Added to results.
  [20250402_172117.]: Solving hyperbolic regression for CpG#3
  Hyperbolic solved: 74.9349254100163
  [20250402_172117.]: Samplename: Sample#1  
  Root: 74.935  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 27.6844381581493
  [20250402_172117.]: Samplename: Sample#10  
  Root: 27.684  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 41.852019114379
  [20250402_172117.]: Samplename: Sample#2  
  Root: 41.852  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 55.8325180209418
  [20250402_172117.]: Samplename: Sample#3  
  Root: 55.833  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 8.03519251633153
  [20250402_172117.]: Samplename: Sample#4  
  Root: 8.035  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 24.1066315721853
  [20250402_172117.]: Samplename: Sample#5  
  Root: 24.107  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.2419820027673
  [20250402_172117.]: Samplename: Sample#6  
  Root: 26.242  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 44.0944922703422
  [20250402_172117.]: Samplename: Sample#7  
  Root: 44.094  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 85.8279382585787
  [20250402_172117.]: Samplename: Sample#8  
  Root: 85.828  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: -0.666482392725758
  [20250402_172117.]: Samplename: Sample#9  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -0.666  
  --> '-10 < root < 0' --> substitute 0
  [20250402_172117.]: Solving hyperbolic regression for CpG#4
  Hyperbolic solved: 76.3495278640236
  [20250402_172117.]: Samplename: Sample#1  
  Root: 76.35  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 28.2568553570941
  [20250402_172117.]: Samplename: Sample#10  
  Root: 28.257  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 43.4089839390807
  [20250402_172117.]: Samplename: Sample#2  
  Root: 43.409  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 58.5435236860146
  [20250402_172117.]: Samplename: Sample#3  
  Root: 58.544  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 10.3087045690571
  [20250402_172117.]: Samplename: Sample#4  
  Root: 10.309  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 22.183045165659
  [20250402_172117.]: Samplename: Sample#5  
  Root: 22.183  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 27.1337769553499
  [20250402_172117.]: Samplename: Sample#6  
  Root: 27.134  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 41.8321096080155
  [20250402_172117.]: Samplename: Sample#7  
  Root: 41.832  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 85.6890189074743
  [20250402_172117.]: Samplename: Sample#8  
  Root: 85.689  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 2.42232098177269
  [20250402_172117.]: Samplename: Sample#9  
  Root: 2.422  
  --> Root in between the borders! Added to results.
  [20250402_172117.]: Solving cubic regression for CpG#5
  Coefficients: -48.4612946127946Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: Sample#1  
  Root: 72.291  
  --> Root in between the borders! Added to results.
  Coefficients: -14.2119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: Sample#10  
  Root: 27.256  
  --> Root in between the borders! Added to results.
  Coefficients: -25.9451041366041Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: Sample#2  
  Root: 44.648  
  --> Root in between the borders! Added to results.
  Coefficients: -32.6879612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: Sample#3  
  Root: 53.538  
  --> Root in between the borders! Added to results.
  Coefficients: -4.69796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: Sample#4  
  Root: 10.206  
  --> Root in between the borders! Added to results.
  Coefficients: -12.0579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: Sample#5  
  Root: 23.695  
  --> Root in between the borders! Added to results.
  Coefficients: -13.9179612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: Sample#6  
  Root: 26.778  
  --> Root in between the borders! Added to results.
  Coefficients: -24.9119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: Sample#7  
  Root: 43.226  
  --> Root in between the borders! Added to results.
  Coefficients: -63.7579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: Sample#8  
  Root: 88.581  
  --> Root in between the borders! Added to results.
  Coefficients: -0.587961279461277Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: Sample#9  
  Root: 1.375  
  --> Root in between the borders! Added to results.
  [20250402_172117.]: Solving hyperbolic regression for CpG#6
  Hyperbolic solved: 79.2780593622711
  [20250402_172117.]: Samplename: Sample#1  
  Root: 79.278  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 30.2012458984074
  [20250402_172117.]: Samplename: Sample#10  
  Root: 30.201  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 41.8474393624107
  [20250402_172117.]: Samplename: Sample#2  
  Root: 41.847  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 56.8423517321508
  [20250402_172117.]: Samplename: Sample#3  
  Root: 56.842  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 8.87856046118588
  [20250402_172117.]: Samplename: Sample#4  
  Root: 8.879  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 18.69015950004
  [20250402_172117.]: Samplename: Sample#5  
  Root: 18.69  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 29.9309263534749
  [20250402_172117.]: Samplename: Sample#6  
  Root: 29.931  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 42.8148560027697
  [20250402_172117.]: Samplename: Sample#7  
  Root: 42.815  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 86.7501831416152
  [20250402_172117.]: Samplename: Sample#8  
  Root: 86.75  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 1.51516194985267
  [20250402_172117.]: Samplename: Sample#9  
  Root: 1.515  
  --> Root in between the borders! Added to results.
  [20250402_172117.]: Solving hyperbolic regression for CpG#7
  Hyperbolic solved: 78.2565592569279
  [20250402_172117.]: Samplename: Sample#1  
  Root: 78.257  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 25.488739349283
  [20250402_172117.]: Samplename: Sample#10  
  Root: 25.489  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 47.3712258915285
  [20250402_172117.]: Samplename: Sample#2  
  Root: 47.371  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 58.3142673189298
  [20250402_172117.]: Samplename: Sample#3  
  Root: 58.314  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 11.7212231360573
  [20250402_172117.]: Samplename: Sample#4  
  Root: 11.721  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 25.3797485992238
  [20250402_172117.]: Samplename: Sample#5  
  Root: 25.38  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 29.4095133062523
  [20250402_172117.]: Samplename: Sample#6  
  Root: 29.41  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 44.5755071469546
  [20250402_172117.]: Samplename: Sample#7  
  Root: 44.576  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 85.9628731021447
  [20250402_172117.]: Samplename: Sample#8  
  Root: 85.963  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: -4.1645647175353
  [20250402_172117.]: Samplename: Sample#9  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -4.165  
  --> '-10 < root < 0' --> substitute 0
  [20250402_172117.]: Solving cubic regression for CpG#8
  Coefficients: -56.4535185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: Sample#1  
  Root: 72.337  
  --> Root in between the borders! Added to results.
  Coefficients: -18.6701851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: Sample#10  
  Root: 28.678  
  --> Root in between the borders! Added to results.
  Coefficients: -24.0387566137566Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: Sample#2  
  Root: 35.595  
  --> Root in between the borders! Added to results.
  Coefficients: -43.9451851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: Sample#3  
  Root: 58.861  
  --> Root in between the borders! Added to results.
  Coefficients: -5.70018518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: Sample#4  
  Root: 9.868  
  --> Root in between the borders! Added to results.
  Coefficients: -12.4851851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: Sample#5  
  Root: 20.166  
  --> Root in between the borders! Added to results.
  Coefficients: -26.8801851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: Sample#6  
  Root: 39.117  
  --> Root in between the borders! Added to results.
  Coefficients: -31.8421851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: Sample#7  
  Root: 45.08  
  --> Root in between the borders! Added to results.
  Coefficients: -68.0081851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: Sample#8  
  Root: 84.373  
  --> Root in between the borders! Added to results.
  Coefficients: 2.07981481481482Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: Sample#9  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -4.026  
  --> '-10 < root < 0' --> substitute 0
  [20250402_172117.]: Solving cubic regression for CpG#9
  Coefficients: -60.8091986531987Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: Sample#1  
  Root: 81.262  
  --> Root in between the borders! Added to results.
  Coefficients: -14.5538653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: Sample#10  
  Root: 24.569  
  --> Root in between the borders! Added to results.
  Coefficients: -26.6344367484368Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: Sample#2  
  Root: 45.035  
  --> Root in between the borders! Added to results.
  Coefficients: -35.4783653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: Sample#3  
  Root: 57.113  
  --> Root in between the borders! Added to results.
  Coefficients: -4.73586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: Sample#4  
  Root: 7.362  
  --> Root in between the borders! Added to results.
  Coefficients: -12.5308653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: Sample#5  
  Root: 20.907  
  --> Root in between the borders! Added to results.
  Coefficients: -21.9358653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: Sample#6  
  Root: 37.545  
  --> Root in between the borders! Added to results.
  Coefficients: -25.1998653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: Sample#7  
  Root: 42.828  
  --> Root in between the borders! Added to results.
  Coefficients: -70.5118653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: Sample#8  
  Root: 88.082  
  --> Root in between the borders! Added to results.
  Coefficients: -0.505865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: Sample#9  
  Root: 0.749  
  --> Root in between the borders! Added to results.
  [20250402_172117.]: Solving hyperbolic regression for row_means
  Hyperbolic solved: 77.0692797356261
  [20250402_172117.]: Samplename: Sample#1  
  Root: 77.069  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 28.3620040447844
  [20250402_172117.]: Samplename: Sample#10  
  Root: 28.362  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 42.5026170660315
  [20250402_172117.]: Samplename: Sample#2  
  Root: 42.503  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 57.2972045344154
  [20250402_172117.]: Samplename: Sample#3  
  Root: 57.297  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 8.82704040274281
  [20250402_172117.]: Samplename: Sample#4  
  Root: 8.827  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 21.8102591233667
  [20250402_172117.]: Samplename: Sample#5  
  Root: 21.81  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 28.722865717687
  [20250402_172117.]: Samplename: Sample#6  
  Root: 28.723  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 43.4105098027891
  [20250402_172117.]: Samplename: Sample#7  
  Root: 43.411  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 86.4143551699061
  [20250402_172117.]: Samplename: Sample#8  
  Root: 86.414  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: -0.237019926848022
  [20250402_172117.]: Samplename: Sample#9  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -0.237  
  --> '-10 < root < 0' --> substitute 0
  [20250402_172117.]: Entered 'solving_equations'-Function
  [20250402_172117.]: Solving hyperbolic regression for CpG#1
  Hyperbolic solved: -2.23222990163966
  [20250402_172117.]: Samplename: 0  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -2.232  
  --> '-10 < root < 0' --> substitute 0
  Hyperbolic solved: 12.1698489850618
  [20250402_172117.]: Samplename: 12.5  
  Root: 12.17  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 24.4781920312644
  [20250402_172117.]: Samplename: 25  
  Root: 24.478  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 38.173044740918
  [20250402_172117.]: Samplename: 37.5  
  Root: 38.173  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 52.3349371964438
  [20250402_172117.]: Samplename: 50  
  Root: 52.335  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 65.4582773627666
  [20250402_172117.]: Samplename: 62.5  
  Root: 65.458  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 75.0090795260796
  [20250402_172117.]: Samplename: 75  
  Root: 75.009  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 81.5271920968417
  [20250402_172117.]: Samplename: 87.5  
  Root: 81.527  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 102.400893095062
  [20250402_172117.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 102.401  
  --> '100 < root < 110' --> substitute 100
  [20250402_172117.]: Solving cubic regression for CpG#2
  Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172117.]: Samplename: 0  
  Root: 0.549  
  --> Root in between the borders! Added to results.
  Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172117.]: Samplename: 12.5  
  Root: 11.533  
  --> Root in between the borders! Added to results.
  Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172117.]: Samplename: 25  
  Root: 26.628  
  --> Root in between the borders! Added to results.
  Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172117.]: Samplename: 37.5  
  Root: 35.509  
  --> Root in between the borders! Added to results.
  Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172117.]: Samplename: 50  
  Root: 47.851  
  --> Root in between the borders! Added to results.
  Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172117.]: Samplename: 62.5  
  Root: 66.893  
  --> Root in between the borders! Added to results.
  Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172117.]: Samplename: 75  
  Root: 75.431  
  --> Root in between the borders! Added to results.
  Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172117.]: Samplename: 87.5  
  Root: 84.118  
  --> Root in between the borders! Added to results.
  Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
  [20250402_172117.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 101.287  
  --> '100 < root < 110' --> substitute 100
  [20250402_172117.]: Solving hyperbolic regression for CpG#3
  Hyperbolic solved: 0.51235653688495
  [20250402_172117.]: Samplename: 0  
  Root: 0.512  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 10.7523884294604
  [20250402_172117.]: Samplename: 12.5  
  Root: 10.752  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 25.5218907947761
  [20250402_172117.]: Samplename: 25  
  Root: 25.522  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 36.5270462675211
  [20250402_172117.]: Samplename: 37.5  
  Root: 36.527  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 50.7909245028224
  [20250402_172117.]: Samplename: 50  
  Root: 50.791  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 64.8686317550184
  [20250402_172117.]: Samplename: 62.5  
  Root: 64.869  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 77.5524188495235
  [20250402_172117.]: Samplename: 75  
  Root: 77.552  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 80.4374617358174
  [20250402_172117.]: Samplename: 87.5  
  Root: 80.437  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 102.704024900825
  [20250402_172117.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 102.704  
  --> '100 < root < 110' --> substitute 100
  [20250402_172117.]: Solving hyperbolic regression for CpG#4
  Hyperbolic solved: -0.519503092357606
  [20250402_172117.]: Samplename: 0  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -0.52  
  --> '-10 < root < 0' --> substitute 0
  Hyperbolic solved: 12.4934147844872
  [20250402_172117.]: Samplename: 12.5  
  Root: 12.493  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 24.2685420024115
  [20250402_172117.]: Samplename: 25  
  Root: 24.269  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 38.0817128465023
  [20250402_172117.]: Samplename: 37.5  
  Root: 38.082  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 48.5843181174811
  [20250402_172117.]: Samplename: 50  
  Root: 48.584  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 67.6722399183037
  [20250402_172117.]: Samplename: 62.5  
  Root: 67.672  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 74.1549277799119
  [20250402_172117.]: Samplename: 75  
  Root: 74.155  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 82.8821797890026
  [20250402_172117.]: Samplename: 87.5  
  Root: 82.882  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 102.0791269023
  [20250402_172117.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 102.079  
  --> '100 < root < 110' --> substitute 100
  [20250402_172117.]: Solving cubic regression for CpG#5
  Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: 0  
  Root: 1.458  
  --> Root in between the borders! Added to results.
  Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: 12.5  
  Root: 10.347  
  --> Root in between the borders! Added to results.
  Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: 25  
  Root: 24.815  
  --> Root in between the borders! Added to results.
  Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: 37.5  
  Root: 37.902  
  --> Root in between the borders! Added to results.
  Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: 50  
  Root: 50.977  
  --> Root in between the borders! Added to results.
  Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: 62.5  
  Root: 62.126  
  --> Root in between the borders! Added to results.
  Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: 75  
  Root: 75.852  
  --> Root in between the borders! Added to results.
  Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: 87.5  
  Root: 85.767  
  --> Root in between the borders! Added to results.
  Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
  [20250402_172117.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 100.743  
  --> '100 < root < 110' --> substitute 100
  [20250402_172117.]: Solving hyperbolic regression for CpG#6
  Hyperbolic solved: 0.138163748613034
  [20250402_172117.]: Samplename: 0  
  Root: 0.138  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 11.8635558881981
  [20250402_172117.]: Samplename: 12.5  
  Root: 11.864  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.5107449550797
  [20250402_172117.]: Samplename: 25  
  Root: 26.511  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 35.3205073050661
  [20250402_172117.]: Samplename: 37.5  
  Root: 35.321  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 50.0570767570666
  [20250402_172117.]: Samplename: 50  
  Root: 50.057  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 64.9602944381018
  [20250402_172117.]: Samplename: 62.5  
  Root: 64.96  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 73.66890571617
  [20250402_172117.]: Samplename: 75  
  Root: 73.669  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 87.1266086585036
  [20250402_172117.]: Samplename: 87.5  
  Root: 87.127  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100.261637014212
  [20250402_172117.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 100.262  
  --> '100 < root < 110' --> substitute 100
  [20250402_172117.]: Solving hyperbolic regression for CpG#7
  Hyperbolic solved: -1.37238087287012
  [20250402_172117.]: Samplename: 0  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -1.372  
  --> '-10 < root < 0' --> substitute 0
  Hyperbolic solved: 10.1993162352498
  [20250402_172117.]: Samplename: 12.5  
  Root: 10.199  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 24.595178967123
  [20250402_172117.]: Samplename: 25  
  Root: 24.595  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 37.8310421041787
  [20250402_172117.]: Samplename: 37.5  
  Root: 37.831  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 53.5588739724067
  [20250402_172117.]: Samplename: 50  
  Root: 53.559  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 65.9364947980258
  [20250402_172117.]: Samplename: 62.5  
  Root: 65.936  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 75.7361094434913
  [20250402_172117.]: Samplename: 75  
  Root: 75.736  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 79.432823759854
  [20250402_172117.]: Samplename: 87.5  
  Root: 79.433  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 103.004237013737
  [20250402_172117.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 103.004  
  --> '100 < root < 110' --> substitute 100
  [20250402_172117.]: Solving cubic regression for CpG#8
  Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: 0  
  Root: 2.016  
  --> Root in between the borders! Added to results.
  Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: 12.5  
  Root: 9.458  
  --> Root in between the borders! Added to results.
  Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: 25  
  Root: 26.35  
  --> Root in between the borders! Added to results.
  Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: 37.5  
  Root: 34.728  
  --> Root in between the borders! Added to results.
  Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: 50  
  Root: 51.781  
  --> Root in between the borders! Added to results.
  Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: 62.5  
  Root: 64.192  
  --> Root in between the borders! Added to results.
  Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: 75  
  Root: 77.804  
  --> Root in between the borders! Added to results.
  Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: 87.5  
  Root: 80.758  
  --> Root in between the borders! Added to results.
  Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
  [20250402_172117.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 102.834  
  --> '100 < root < 110' --> substitute 100
  [20250402_172117.]: Solving cubic regression for CpG#9
  Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: 0  
  Root: 1.475  
  --> Root in between the borders! Added to results.
  Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: 12.5  
  Root: 10.12  
  --> Root in between the borders! Added to results.
  Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: 25  
  Root: 24.844  
  --> Root in between the borders! Added to results.
  Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: 37.5  
  Root: 35.327  
  --> Root in between the borders! Added to results.
  Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: 50  
  Root: 51.855  
  --> Root in between the borders! Added to results.
  Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: 62.5  
  Root: 65.265  
  --> Root in between the borders! Added to results.
  Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: 75  
  Root: 74.915  
  --> Root in between the borders! Added to results.
  Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: 87.5  
  Root: 84.67  
  --> Root in between the borders! Added to results.
  Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
  [20250402_172117.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 101.082  
  --> '100 < root < 110' --> substitute 100
  [20250402_172117.]: Solving hyperbolic regression for row_means
  Hyperbolic solved: 0.290941088603071
  [20250402_172117.]: Samplename: 0  
  Root: 0.291  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 11.0412408065783
  [20250402_172117.]: Samplename: 12.5  
  Root: 11.041  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 25.4081501047696
  [20250402_172117.]: Samplename: 25  
  Root: 25.408  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 36.5243719024532
  [20250402_172117.]: Samplename: 37.5  
  Root: 36.524  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 50.7348824329668
  [20250402_172117.]: Samplename: 50  
  Root: 50.735  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 65.3135209766198
  [20250402_172117.]: Samplename: 62.5  
  Root: 65.314  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 75.5342709041132
  [20250402_172117.]: Samplename: 75  
  Root: 75.534  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 83.2411228425212
  [20250402_172117.]: Samplename: 87.5  
  Root: 83.241  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 101.666942781592
  [20250402_172117.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 101.667  
  --> '100 < root < 110' --> substitute 100
  [20250402_172118.]: Entered 'clean_dt'-Function
  [20250402_172118.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
  [20250402_172118.]: got experimental data
  [20250402_172118.]: Entered 'clean_dt'-Function
  [20250402_172118.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
  [20250402_172118.]: got calibration data
  [20250402_172118.]: ### Starting with regression calculations ###
  [20250402_172118.]: Entered 'regression_type1'-Function
  [20250402_172118.]: # CpG-site: CpG#1
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172118.]: Logging df_agg: CpG#1
  [20250402_172118.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172118.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_172118.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172118.]: Entered 'hyperbolic_regression'-Function
  [20250402_172118.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172119.]: Entered 'cubic_regression'-Function
  [20250402_172119.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172119.]: # CpG-site: CpG#2
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172119.]: Logging df_agg: CpG#2
  [20250402_172119.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172119.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_172119.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172119.]: Entered 'hyperbolic_regression'-Function
  [20250402_172119.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172119.]: Entered 'cubic_regression'-Function
  [20250402_172119.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172119.]: # CpG-site: CpG#3
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172119.]: Logging df_agg: CpG#3
  [20250402_172119.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172119.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_172119.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172119.]: Entered 'hyperbolic_regression'-Function
  [20250402_172119.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172119.]: Entered 'cubic_regression'-Function
  [20250402_172119.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172119.]: # CpG-site: CpG#4
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172119.]: Logging df_agg: CpG#4
  [20250402_172119.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172119.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_172119.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172119.]: Entered 'hyperbolic_regression'-Function
  [20250402_172119.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172120.]: Entered 'cubic_regression'-Function
  [20250402_172120.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172120.]: # CpG-site: CpG#5
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172120.]: Logging df_agg: CpG#5
  [20250402_172120.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172120.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_172120.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172120.]: Entered 'hyperbolic_regression'-Function
  [20250402_172120.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172120.]: Entered 'cubic_regression'-Function
  [20250402_172120.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172119.]: # CpG-site: CpG#6
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172119.]: Logging df_agg: CpG#6
  [20250402_172119.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172119.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_172119.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172119.]: Entered 'hyperbolic_regression'-Function
  [20250402_172119.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172119.]: Entered 'cubic_regression'-Function
  [20250402_172119.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172119.]: # CpG-site: CpG#7
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172119.]: Logging df_agg: CpG#7
  [20250402_172119.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172119.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_172119.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172119.]: Entered 'hyperbolic_regression'-Function
  [20250402_172119.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172119.]: Entered 'cubic_regression'-Function
  [20250402_172119.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172119.]: # CpG-site: CpG#8
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172119.]: Logging df_agg: CpG#8
  [20250402_172119.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172119.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_172119.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172119.]: Entered 'hyperbolic_regression'-Function
  [20250402_172119.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172120.]: Entered 'cubic_regression'-Function
  [20250402_172120.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172120.]: # CpG-site: CpG#9
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172120.]: Logging df_agg: CpG#9
  [20250402_172120.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172120.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_172120.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172120.]: Entered 'hyperbolic_regression'-Function
  [20250402_172120.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172120.]: Entered 'cubic_regression'-Function
  [20250402_172120.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172120.]: # CpG-site: row_means
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172120.]: Logging df_agg: row_means
  [20250402_172120.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172120.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_172120.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172120.]: Entered 'hyperbolic_regression'-Function
  [20250402_172120.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172120.]: Entered 'cubic_regression'-Function
  [20250402_172120.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172122.]: Entered 'regression_type1'-Function
  [20250402_172123.]: # CpG-site: CpG#1
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172123.]: Logging df_agg: CpG#1
  [20250402_172123.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172123.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_172123.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172123.]: Entered 'hyperbolic_regression'-Function
  [20250402_172123.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172124.]: Entered 'cubic_regression'-Function
  [20250402_172124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172124.]: # CpG-site: CpG#2
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172124.]: Logging df_agg: CpG#2
  [20250402_172124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172124.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_172124.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172124.]: Entered 'hyperbolic_regression'-Function
  [20250402_172124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172124.]: Entered 'cubic_regression'-Function
  [20250402_172124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172124.]: # CpG-site: CpG#3
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172124.]: Logging df_agg: CpG#3
  [20250402_172124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172124.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_172124.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172124.]: Entered 'hyperbolic_regression'-Function
  [20250402_172124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172124.]: Entered 'cubic_regression'-Function
  [20250402_172124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172124.]: # CpG-site: CpG#4
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172124.]: Logging df_agg: CpG#4
  [20250402_172124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172124.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_172124.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172124.]: Entered 'hyperbolic_regression'-Function
  [20250402_172124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172125.]: Entered 'cubic_regression'-Function
  [20250402_172125.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172125.]: # CpG-site: CpG#5
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172125.]: Logging df_agg: CpG#5
  [20250402_172125.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172125.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_172125.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172125.]: Entered 'hyperbolic_regression'-Function
  [20250402_172125.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172125.]: Entered 'cubic_regression'-Function
  [20250402_172125.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172123.]: # CpG-site: CpG#6
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172124.]: Logging df_agg: CpG#6
  [20250402_172124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172124.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_172124.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172124.]: Entered 'hyperbolic_regression'-Function
  [20250402_172124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172124.]: Entered 'cubic_regression'-Function
  [20250402_172124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172124.]: # CpG-site: CpG#7
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172124.]: Logging df_agg: CpG#7
  [20250402_172124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172124.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_172124.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172124.]: Entered 'hyperbolic_regression'-Function
  [20250402_172124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172124.]: Entered 'cubic_regression'-Function
  [20250402_172124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172124.]: # CpG-site: CpG#8
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172124.]: Logging df_agg: CpG#8
  [20250402_172124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172124.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_172124.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172124.]: Entered 'hyperbolic_regression'-Function
  [20250402_172124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172125.]: Entered 'cubic_regression'-Function
  [20250402_172125.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172125.]: # CpG-site: CpG#9
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172125.]: Logging df_agg: CpG#9
  [20250402_172125.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172125.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_172125.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172125.]: Entered 'hyperbolic_regression'-Function
  [20250402_172125.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172125.]: Entered 'cubic_regression'-Function
  [20250402_172125.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172125.]: # CpG-site: row_means
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172125.]: Logging df_agg: row_means
  [20250402_172125.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172125.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_172125.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172125.]: Entered 'hyperbolic_regression'-Function
  [20250402_172125.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172125.]: Entered 'cubic_regression'-Function
  [20250402_172125.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172127.]: Entered 'clean_dt'-Function
  [20250402_172127.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
  [20250402_172127.]: got experimental data
  [20250402_172127.]: Entered 'clean_dt'-Function
  [20250402_172127.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
  [20250402_172127.]: got calibration data
  [20250402_172127.]: ### Starting with regression calculations ###
  [20250402_172127.]: Entered 'regression_type1'-Function
  [20250402_172127.]: # CpG-site: CpG#1
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172128.]: Logging df_agg: CpG#1
  [20250402_172128.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172128.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_172128.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172128.]: Entered 'hyperbolic_regression'-Function
  [20250402_172128.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172128.]: Entered 'cubic_regression'-Function
  [20250402_172128.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172128.]: # CpG-site: CpG#2
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172128.]: Logging df_agg: CpG#2
  [20250402_172128.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172128.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_172128.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172128.]: Entered 'hyperbolic_regression'-Function
  [20250402_172128.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172128.]: Entered 'cubic_regression'-Function
  [20250402_172128.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172128.]: # CpG-site: CpG#3
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172128.]: Logging df_agg: CpG#3
  [20250402_172128.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172128.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_172128.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172128.]: Entered 'hyperbolic_regression'-Function
  [20250402_172129.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172129.]: Entered 'cubic_regression'-Function
  [20250402_172129.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172129.]: # CpG-site: CpG#4
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172129.]: Logging df_agg: CpG#4
  [20250402_172129.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172129.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_172129.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172129.]: Entered 'hyperbolic_regression'-Function
  [20250402_172129.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172129.]: Entered 'cubic_regression'-Function
  [20250402_172129.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172129.]: # CpG-site: CpG#5
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172129.]: Logging df_agg: CpG#5
  [20250402_172129.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172129.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_172129.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172129.]: Entered 'hyperbolic_regression'-Function
  [20250402_172129.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172130.]: Entered 'cubic_regression'-Function
  [20250402_172130.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172128.]: # CpG-site: CpG#6
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172128.]: Logging df_agg: CpG#6
  [20250402_172128.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172128.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_172128.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172128.]: Entered 'hyperbolic_regression'-Function
  [20250402_172128.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172129.]: Entered 'cubic_regression'-Function
  [20250402_172129.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172129.]: # CpG-site: CpG#7
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172129.]: Logging df_agg: CpG#7
  [20250402_172129.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172129.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_172129.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172129.]: Entered 'hyperbolic_regression'-Function
  [20250402_172129.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172129.]: Entered 'cubic_regression'-Function
  [20250402_172129.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172129.]: # CpG-site: CpG#8
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172129.]: Logging df_agg: CpG#8
  [20250402_172129.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172129.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_172129.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172129.]: Entered 'hyperbolic_regression'-Function
  [20250402_172129.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172129.]: Entered 'cubic_regression'-Function
  [20250402_172129.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172129.]: # CpG-site: CpG#9
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172129.]: Logging df_agg: CpG#9
  [20250402_172129.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172129.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_172129.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172129.]: Entered 'hyperbolic_regression'-Function
  [20250402_172129.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172129.]: Entered 'cubic_regression'-Function
  [20250402_172129.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172130.]: # CpG-site: row_means
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172130.]: Logging df_agg: row_means
  [20250402_172130.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172130.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_172130.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172130.]: Entered 'hyperbolic_regression'-Function
  [20250402_172130.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172130.]: Entered 'cubic_regression'-Function
  [20250402_172130.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172132.]: Entered 'regression_type1'-Function
  [20250402_172133.]: # CpG-site: CpG#1
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172134.]: Logging df_agg: CpG#1
  [20250402_172134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172134.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_172134.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
  [20250402_172134.]: Entered 'hyperbolic_regression'-Function
  [20250402_172134.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172134.]: Entered 'cubic_regression'-Function
  [20250402_172134.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172134.]: # CpG-site: CpG#2
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172134.]: Logging df_agg: CpG#2
  [20250402_172134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172134.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_172134.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
  [20250402_172134.]: Entered 'hyperbolic_regression'-Function
  [20250402_172134.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172134.]: Entered 'cubic_regression'-Function
  [20250402_172134.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172134.]: # CpG-site: CpG#3
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172134.]: Logging df_agg: CpG#3
  [20250402_172134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172134.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_172134.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
  [20250402_172134.]: Entered 'hyperbolic_regression'-Function
  [20250402_172134.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172135.]: Entered 'cubic_regression'-Function
  [20250402_172135.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172135.]: # CpG-site: CpG#4
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172135.]: Logging df_agg: CpG#4
  [20250402_172135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172135.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_172135.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
  [20250402_172135.]: Entered 'hyperbolic_regression'-Function
  [20250402_172135.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172135.]: Entered 'cubic_regression'-Function
  [20250402_172135.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172135.]: # CpG-site: CpG#5
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172135.]: Logging df_agg: CpG#5
  [20250402_172135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172135.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_172135.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
  [20250402_172135.]: Entered 'hyperbolic_regression'-Function
  [20250402_172135.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172135.]: Entered 'cubic_regression'-Function
  [20250402_172135.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172134.]: # CpG-site: CpG#6
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172134.]: Logging df_agg: CpG#6
  [20250402_172134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172134.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_172134.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
  [20250402_172134.]: Entered 'hyperbolic_regression'-Function
  [20250402_172134.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172134.]: Entered 'cubic_regression'-Function
  [20250402_172134.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172134.]: # CpG-site: CpG#7
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172134.]: Logging df_agg: CpG#7
  [20250402_172134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172134.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_172134.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
  [20250402_172134.]: Entered 'hyperbolic_regression'-Function
  [20250402_172134.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172134.]: Entered 'cubic_regression'-Function
  [20250402_172134.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172134.]: # CpG-site: CpG#8
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172134.]: Logging df_agg: CpG#8
  [20250402_172134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172134.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_172134.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
  [20250402_172134.]: Entered 'hyperbolic_regression'-Function
  [20250402_172134.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172135.]: Entered 'cubic_regression'-Function
  [20250402_172135.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172135.]: # CpG-site: CpG#9
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172135.]: Logging df_agg: CpG#9
  [20250402_172135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172135.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_172135.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
  [20250402_172135.]: Entered 'hyperbolic_regression'-Function
  [20250402_172135.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172135.]: Entered 'cubic_regression'-Function
  [20250402_172135.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172135.]: # CpG-site: row_means
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172135.]: Logging df_agg: row_means
  [20250402_172135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172135.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_172135.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
  [20250402_172135.]: Entered 'hyperbolic_regression'-Function
  [20250402_172135.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172135.]: Entered 'cubic_regression'-Function
  [20250402_172135.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172136.]: Entered 'solving_equations'-Function
  [20250402_172136.]: Solving hyperbolic regression for CpG#1
  Hyperbolic solved: 0
  [20250402_172136.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 14.1381159662486
  [20250402_172136.]: Samplename: 12.5  
  Root: 14.138  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.1241053609707
  [20250402_172136.]: Samplename: 25  
  Root: 26.124  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 39.3567419170867
  [20250402_172136.]: Samplename: 37.5  
  Root: 39.357  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 52.9273107806133
  [20250402_172136.]: Samplename: 50  
  Root: 52.927  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 65.4010628999278
  [20250402_172136.]: Samplename: 62.5  
  Root: 65.401  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 74.4183184249663
  [20250402_172136.]: Samplename: 75  
  Root: 74.418  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 80.5431520527512
  [20250402_172136.]: Samplename: 87.5  
  Root: 80.543  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172136.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172136.]: Solving hyperbolic regression for CpG#2
  Hyperbolic solved: 0
  [20250402_172136.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 10.7851657015183
  [20250402_172136.]: Samplename: 12.5  
  Root: 10.785  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.0727152156421
  [20250402_172136.]: Samplename: 25  
  Root: 26.073  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 35.2074258210424
  [20250402_172136.]: Samplename: 37.5  
  Root: 35.207  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 47.9305924748583
  [20250402_172136.]: Samplename: 50  
  Root: 47.931  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 67.2847555363015
  [20250402_172136.]: Samplename: 62.5  
  Root: 67.285  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 75.735332403378
  [20250402_172136.]: Samplename: 75  
  Root: 75.735  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 84.1313047876192
  [20250402_172136.]: Samplename: 87.5  
  Root: 84.131  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172136.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172136.]: Solving hyperbolic regression for CpG#3
  Hyperbolic solved: 0
  [20250402_172136.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 10.8497990553835
  [20250402_172136.]: Samplename: 12.5  
  Root: 10.85  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.1511183533449
  [20250402_172136.]: Samplename: 25  
  Root: 26.151  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 37.2940213300522
  [20250402_172136.]: Samplename: 37.5  
  Root: 37.294  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 51.419361136507
  [20250402_172136.]: Samplename: 50  
  Root: 51.419  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 65.0212050873619
  [20250402_172136.]: Samplename: 62.5  
  Root: 65.021  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 76.9977789568509
  [20250402_172136.]: Samplename: 75  
  Root: 76.998  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 79.686036177122
  [20250402_172136.]: Samplename: 87.5  
  Root: 79.686  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172136.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172136.]: Solving hyperbolic regression for CpG#4
  Hyperbolic solved: 0
  [20250402_172136.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 13.2434477796981
  [20250402_172136.]: Samplename: 12.5  
  Root: 13.243  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 25.0815867666892
  [20250402_172136.]: Samplename: 25  
  Root: 25.082  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 38.7956859187734
  [20250402_172136.]: Samplename: 37.5  
  Root: 38.796  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 49.1001600195185
  [20250402_172136.]: Samplename: 50  
  Root: 49.1  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 67.5620415214226
  [20250402_172136.]: Samplename: 62.5  
  Root: 67.562  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 73.7554076043322
  [20250402_172136.]: Samplename: 75  
  Root: 73.755  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 82.0327440839301
  [20250402_172136.]: Samplename: 87.5  
  Root: 82.033  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172136.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172136.]: Solving hyperbolic regression for CpG#5
  Hyperbolic solved: 0
  [20250402_172136.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 8.36665146544904
  [20250402_172136.]: Samplename: 12.5  
  Root: 8.367  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 23.0855280383989
  [20250402_172136.]: Samplename: 25  
  Root: 23.086  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 37.0098400819818
  [20250402_172136.]: Samplename: 37.5  
  Root: 37.01  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 51.0085868408378
  [20250402_172136.]: Samplename: 50  
  Root: 51.009  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 62.7441416833696
  [20250402_172136.]: Samplename: 62.5  
  Root: 62.744  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 76.6857826005162
  [20250402_172136.]: Samplename: 75  
  Root: 76.686  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 86.3046084696663
  [20250402_172136.]: Samplename: 87.5  
  Root: 86.305  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172136.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172136.]: Solving hyperbolic regression for CpG#6
  Hyperbolic solved: 0
  [20250402_172136.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 11.822687731114
  [20250402_172136.]: Samplename: 12.5  
  Root: 11.823  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.5494368772504
  [20250402_172136.]: Samplename: 25  
  Root: 26.549  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 35.3846787677878
  [20250402_172136.]: Samplename: 37.5  
  Root: 35.385  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 50.1264563333089
  [20250402_172136.]: Samplename: 50  
  Root: 50.126  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 64.9875101866844
  [20250402_172136.]: Samplename: 62.5  
  Root: 64.988  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 73.6494948240195
  [20250402_172136.]: Samplename: 75  
  Root: 73.649  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 87.0033714659226
  [20250402_172136.]: Samplename: 87.5  
  Root: 87.003  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172136.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172136.]: Solving hyperbolic regression for CpG#7
  Hyperbolic solved: 0
  [20250402_172136.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 11.7925453863418
  [20250402_172136.]: Samplename: 12.5  
  Root: 11.793  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.2042827174053
  [20250402_172136.]: Samplename: 25  
  Root: 26.204  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 39.2081609373531
  [20250402_172136.]: Samplename: 37.5  
  Root: 39.208  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 54.3620766326312
  [20250402_172136.]: Samplename: 50  
  Root: 54.362  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 66.0664882334621
  [20250402_172136.]: Samplename: 62.5  
  Root: 66.066  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 75.1981507250883
  [20250402_172136.]: Samplename: 75  
  Root: 75.198  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 78.6124357632637
  [20250402_172136.]: Samplename: 87.5  
  Root: 78.612  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172136.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172136.]: Solving hyperbolic regression for CpG#8
  Hyperbolic solved: 0
  [20250402_172136.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 7.27736114274885
  [20250402_172136.]: Samplename: 12.5  
  Root: 7.277  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 24.9863834890886
  [20250402_172136.]: Samplename: 25  
  Root: 24.986  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 34.0400823094579
  [20250402_172136.]: Samplename: 37.5  
  Root: 34.04  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 52.3077192847199
  [20250402_172136.]: Samplename: 50  
  Root: 52.308  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 65.0861558866387
  [20250402_172136.]: Samplename: 62.5  
  Root: 65.086  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 78.3136588178128
  [20250402_172136.]: Samplename: 75  
  Root: 78.314  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 81.058248740059
  [20250402_172136.]: Samplename: 87.5  
  Root: 81.058  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172136.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172136.]: Solving hyperbolic regression for CpG#9
  Hyperbolic solved: 0
  [20250402_172136.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 12.2094906593745
  [20250402_172136.]: Samplename: 12.5  
  Root: 12.209  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 28.0738986154201
  [20250402_172136.]: Samplename: 25  
  Root: 28.074  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 37.6720254587223
  [20250402_172136.]: Samplename: 37.5  
  Root: 37.672  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 52.3746308870569
  [20250402_172136.]: Samplename: 50  
  Root: 52.375  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 64.8693631845077
  [20250402_172136.]: Samplename: 62.5  
  Root: 64.869  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 74.2598902601534
  [20250402_172136.]: Samplename: 75  
  Root: 74.26  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 83.9376844048195
  [20250402_172136.]: Samplename: 87.5  
  Root: 83.938  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172136.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172136.]: Solving hyperbolic regression for row_means
  Hyperbolic solved: 0
  [20250402_172136.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 11.1506882890389
  [20250402_172136.]: Samplename: 12.5  
  Root: 11.151  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 25.841636381907
  [20250402_172136.]: Samplename: 25  
  Root: 25.842  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 37.0462679509085
  [20250402_172136.]: Samplename: 37.5  
  Root: 37.046  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 51.1681297765954
  [20250402_172136.]: Samplename: 50  
  Root: 51.168  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 65.4258217891781
  [20250402_172136.]: Samplename: 62.5  
  Root: 65.426  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 75.285632789037
  [20250402_172136.]: Samplename: 75  
  Root: 75.286  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 82.6475419323379
  [20250402_172136.]: Samplename: 87.5  
  Root: 82.648  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172136.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172136.]: ### Starting with regression calculations ###
  [20250402_172136.]: Entered 'regression_type1'-Function
  [20250402_172137.]: # CpG-site: CpG#1
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
  [20250402_172137.]: Logging df_agg: CpG#1
  [20250402_172137.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172137.]: c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
  [20250402_172137.]: Entered 'hyperbolic_regression'-Function
  [20250402_172137.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172137.]: Entered 'cubic_regression'-Function
  [20250402_172137.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172137.]: # CpG-site: CpG#2
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
  [20250402_172137.]: Logging df_agg: CpG#2
  [20250402_172137.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172137.]: c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
  [20250402_172137.]: Entered 'hyperbolic_regression'-Function
  [20250402_172137.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172138.]: Entered 'cubic_regression'-Function
  [20250402_172138.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172138.]: # CpG-site: CpG#3
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
  [20250402_172138.]: Logging df_agg: CpG#3
  [20250402_172138.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172138.]: c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
  [20250402_172138.]: Entered 'hyperbolic_regression'-Function
  [20250402_172138.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172138.]: Entered 'cubic_regression'-Function
  [20250402_172138.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172138.]: # CpG-site: CpG#4
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
  [20250402_172138.]: Logging df_agg: CpG#4
  [20250402_172138.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172138.]: c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
  [20250402_172138.]: Entered 'hyperbolic_regression'-Function
  [20250402_172138.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172138.]: Entered 'cubic_regression'-Function
  [20250402_172138.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172139.]: # CpG-site: CpG#5
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
  [20250402_172139.]: Logging df_agg: CpG#5
  [20250402_172139.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172139.]: c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
  [20250402_172139.]: Entered 'hyperbolic_regression'-Function
  [20250402_172139.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172139.]: Entered 'cubic_regression'-Function
  [20250402_172139.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172137.]: # CpG-site: CpG#6
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
  [20250402_172138.]: Logging df_agg: CpG#6
  [20250402_172138.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172138.]: c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
  [20250402_172138.]: Entered 'hyperbolic_regression'-Function
  [20250402_172138.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172138.]: Entered 'cubic_regression'-Function
  [20250402_172138.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172138.]: # CpG-site: CpG#7
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
  [20250402_172138.]: Logging df_agg: CpG#7
  [20250402_172138.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172138.]: c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
  [20250402_172138.]: Entered 'hyperbolic_regression'-Function
  [20250402_172138.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172138.]: Entered 'cubic_regression'-Function
  [20250402_172138.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172138.]: # CpG-site: CpG#8
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
  [20250402_172138.]: Logging df_agg: CpG#8
  [20250402_172138.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172138.]: c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
  [20250402_172138.]: Entered 'hyperbolic_regression'-Function
  [20250402_172138.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172139.]: Entered 'cubic_regression'-Function
  [20250402_172139.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172139.]: # CpG-site: CpG#9
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
  [20250402_172139.]: Logging df_agg: CpG#9
  [20250402_172139.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172139.]: c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
  [20250402_172139.]: Entered 'hyperbolic_regression'-Function
  [20250402_172139.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172139.]: Entered 'cubic_regression'-Function
  [20250402_172139.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172139.]: # CpG-site: row_means
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
  [20250402_172139.]: Logging df_agg: row_means
  [20250402_172139.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172139.]: c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
  [20250402_172139.]: Entered 'hyperbolic_regression'-Function
  [20250402_172139.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172139.]: Entered 'cubic_regression'-Function
  [20250402_172139.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172140.]: Entered 'solving_equations'-Function
  [20250402_172140.]: Solving cubic regression for CpG#1
  Coefficients: 0Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
  [20250402_172140.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Coefficients: -7.30533333333333Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
  [20250402_172140.]: Samplename: 12.5  
  Root: 10.279  
  --> Root in between the borders! Added to results.
  Coefficients: -14.352Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
  [20250402_172140.]: Samplename: 25  
  Root: 21.591  
  --> Root in between the borders! Added to results.
  Coefficients: -23.244Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
  [20250402_172140.]: Samplename: 37.5  
  Root: 36.617  
  --> Root in between the borders! Added to results.
  Coefficients: -33.8645Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
  [20250402_172140.]: Samplename: 50  
  Root: 52.729  
  --> Root in between the borders! Added to results.
  Coefficients: -45.318Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
  [20250402_172140.]: Samplename: 62.5  
  Root: 66.532  
  --> Root in between the borders! Added to results.
  Coefficients: -54.857Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
  [20250402_172140.]: Samplename: 75  
  Root: 75.773  
  --> Root in between the borders! Added to results.
  Coefficients: -62.062Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
  [20250402_172140.]: Samplename: 87.5  
  Root: 81.772  
  --> Root in between the borders! Added to results.
  Coefficients: -90.01Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
  [20250402_172140.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172140.]: Solving cubic regression for CpG#2
  Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172140.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172140.]: Samplename: 12.5  
  Root: 10.991  
  --> Root in between the borders! Added to results.
  Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172140.]: Samplename: 25  
  Root: 26.435  
  --> Root in between the borders! Added to results.
  Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172140.]: Samplename: 37.5  
  Root: 35.545  
  --> Root in between the borders! Added to results.
  Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172140.]: Samplename: 50  
  Root: 48.102  
  --> Root in between the borders! Added to results.
  Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172140.]: Samplename: 62.5  
  Root: 67.086  
  --> Root in between the borders! Added to results.
  Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172140.]: Samplename: 75  
  Root: 75.419  
  --> Root in between the borders! Added to results.
  Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172140.]: Samplename: 87.5  
  Root: 83.785  
  --> Root in between the borders! Added to results.
  Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172140.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172140.]: Solving cubic regression for CpG#3
  Coefficients: 0Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
  [20250402_172140.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Coefficients: -5.67Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
  [20250402_172140.]: Samplename: 12.5  
  Root: 9.387  
  --> Root in between the borders! Added to results.
  Coefficients: -14.526Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
  [20250402_172140.]: Samplename: 25  
  Root: 24.373  
  --> Root in between the borders! Added to results.
  Coefficients: -21.71Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
  [20250402_172140.]: Samplename: 37.5  
  Root: 36.135  
  --> Root in between the borders! Added to results.
  Coefficients: -31.8725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
  [20250402_172140.]: Samplename: 50  
  Root: 51.29  
  --> Root in between the borders! Added to results.
  Coefficients: -42.986Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
  [20250402_172140.]: Samplename: 62.5  
  Root: 65.561  
  --> Root in between the borders! Added to results.
  Coefficients: -54.0725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
  [20250402_172140.]: Samplename: 75  
  Root: 77.683  
  --> Root in between the borders! Added to results.
  Coefficients: -56.7533333333333Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
  [20250402_172140.]: Samplename: 87.5  
  Root: 80.348  
  --> Root in between the borders! Added to results.
  Coefficients: -79.762Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
  [20250402_172140.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172140.]: Solving cubic regression for CpG#4
  Coefficients: 0Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
  [20250402_172140.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Coefficients: -7.65533333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
  [20250402_172140.]: Samplename: 12.5  
  Root: 11.333  
  --> Root in between the borders! Added to results.
  Coefficients: -15.206Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
  [20250402_172140.]: Samplename: 25  
  Root: 22.933  
  --> Root in between the borders! Added to results.
  Coefficients: -24.93Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
  [20250402_172140.]: Samplename: 37.5  
  Root: 37.542  
  --> Root in between the borders! Added to results.
  Coefficients: -33.0395Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
  [20250402_172140.]: Samplename: 50  
  Root: 48.772  
  --> Root in between the borders! Added to results.
  Coefficients: -49.658Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
  [20250402_172140.]: Samplename: 62.5  
  Root: 68.324  
  --> Root in between the borders! Added to results.
  Coefficients: -55.942Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
  [20250402_172140.]: Samplename: 75  
  Root: 74.614  
  --> Root in between the borders! Added to results.
  Coefficients: -64.9953333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
  [20250402_172140.]: Samplename: 87.5  
  Root: 82.816  
  --> Root in between the borders! Added to results.
  Coefficients: -87.724Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
  [20250402_172140.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172140.]: Solving cubic regression for CpG#5
  Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172140.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172140.]: Samplename: 12.5  
  Root: 9.593  
  --> Root in between the borders! Added to results.
  Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172140.]: Samplename: 25  
  Root: 24.704  
  --> Root in between the borders! Added to results.
  Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172140.]: Samplename: 37.5  
  Root: 38.051  
  --> Root in between the borders! Added to results.
  Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172140.]: Samplename: 50  
  Root: 51.187  
  --> Root in between the borders! Added to results.
  Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172140.]: Samplename: 62.5  
  Root: 62.269  
  --> Root in between the borders! Added to results.
  Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172140.]: Samplename: 75  
  Root: 75.786  
  --> Root in between the borders! Added to results.
  Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172140.]: Samplename: 87.5  
  Root: 85.475  
  --> Root in between the borders! Added to results.
  Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172140.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 100  
  --> '100 < root < 110' --> substitute 100
  [20250402_172140.]: Solving cubic regression for CpG#6
  Coefficients: 0Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
  [20250402_172140.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Coefficients: -6.54266666666667Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
  [20250402_172140.]: Samplename: 12.5  
  Root: 11.495  
  --> Root in between the borders! Added to results.
  Coefficients: -15.692Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
  [20250402_172140.]: Samplename: 25  
  Root: 26.346  
  --> Root in between the borders! Added to results.
  Coefficients: -21.804Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
  [20250402_172140.]: Samplename: 37.5  
  Root: 35.332  
  --> Root in between the borders! Added to results.
  Coefficients: -33.2485Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
  [20250402_172140.]: Samplename: 50  
  Root: 50.228  
  --> Root in between the borders! Added to results.
  Coefficients: -46.704Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
  [20250402_172140.]: Samplename: 62.5  
  Root: 65.055  
  --> Root in between the borders! Added to results.
  Coefficients: -55.636Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
  [20250402_172140.]: Samplename: 75  
  Root: 73.641  
  --> Root in between the borders! Added to results.
  Coefficients: -71.3493333333333Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
  [20250402_172140.]: Samplename: 87.5  
  Root: 86.903  
  --> Root in between the borders! Added to results.
  Coefficients: -89.46Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
  [20250402_172140.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172140.]: Solving cubic regression for CpG#7
  Coefficients: 0Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
  [20250402_172140.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Coefficients: -4.18066666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
  [20250402_172140.]: Samplename: 12.5  
  Root: 8.108  
  --> Root in between the borders! Added to results.
  Coefficients: -10.05Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
  [20250402_172140.]: Samplename: 25  
  Root: 21.288  
  --> Root in between the borders! Added to results.
  Coefficients: -16.236Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
  [20250402_172140.]: Samplename: 37.5  
  Root: 36.173  
  --> Root in between the borders! Added to results.
  Coefficients: -24.8165Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
  [20250402_172140.]: Samplename: 50  
  Root: 54.247  
  --> Root in between the borders! Added to results.
  Coefficients: -32.75Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
  [20250402_172140.]: Samplename: 62.5  
  Root: 67.087  
  --> Root in between the borders! Added to results.
  Coefficients: -39.954Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
  [20250402_172140.]: Samplename: 75  
  Root: 76.377  
  --> Root in between the borders! Added to results.
  Coefficients: -42.9206666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
  [20250402_172140.]: Samplename: 87.5  
  Root: 79.728  
  --> Root in between the borders! Added to results.
  Coefficients: -66.008Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
  [20250402_172140.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172140.]: Solving cubic regression for CpG#8
  Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172140.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172140.]: Samplename: 12.5  
  Root: 8.039  
  --> Root in between the borders! Added to results.
  Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172140.]: Samplename: 25  
  Root: 26.079  
  --> Root in between the borders! Added to results.
  Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172140.]: Samplename: 37.5  
  Root: 34.864  
  --> Root in between the borders! Added to results.
  Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172140.]: Samplename: 50  
  Root: 52.311  
  --> Root in between the borders! Added to results.
  Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172140.]: Samplename: 62.5  
  Root: 64.584  
  --> Root in between the borders! Added to results.
  Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172140.]: Samplename: 75  
  Root: 77.576  
  --> Root in between the borders! Added to results.
  Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172140.]: Samplename: 87.5  
  Root: 80.326  
  --> Root in between the borders! Added to results.
  Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172140.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 100  
  --> '100 < root < 110' --> substitute 100
  [20250402_172140.]: Solving cubic regression for CpG#9
  Coefficients: 0Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
  [20250402_172140.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Coefficients: -5.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
  [20250402_172140.]: Samplename: 12.5  
  Root: 8.93  
  --> Root in between the borders! Added to results.
  Coefficients: -13.716Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
  [20250402_172140.]: Samplename: 25  
  Root: 24.492  
  --> Root in between the borders! Added to results.
  Coefficients: -19.634Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
  [20250402_172140.]: Samplename: 37.5  
  Root: 35.53  
  --> Root in between the borders! Added to results.
  Coefficients: -30.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
  [20250402_172140.]: Samplename: 50  
  Root: 52.349  
  --> Root in between the borders! Added to results.
  Coefficients: -41.696Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
  [20250402_172140.]: Samplename: 62.5  
  Root: 65.528  
  --> Root in between the borders! Added to results.
  Coefficients: -51.9135Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
  [20250402_172140.]: Samplename: 75  
  Root: 74.87  
  --> Root in between the borders! Added to results.
  Coefficients: -64.5026666666667Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
  [20250402_172140.]: Samplename: 87.5  
  Root: 84.256  
  --> Root in between the borders! Added to results.
  Coefficients: -92Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
  [20250402_172140.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172140.]: Solving cubic regression for row_means
  Coefficients: 0Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
  [20250402_172140.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Coefficients: -5.70125925925926Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
  [20250402_172140.]: Samplename: 12.5  
  Root: 9.866  
  --> Root in between the borders! Added to results.
  Coefficients: -14.126Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
  [20250402_172140.]: Samplename: 25  
  Root: 24.413  
  --> Root in between the borders! Added to results.
  Coefficients: -21.378Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
  [20250402_172140.]: Samplename: 37.5  
  Root: 36.177  
  --> Root in between the borders! Added to results.
  Coefficients: -31.7547777777778Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
  [20250402_172140.]: Samplename: 50  
  Root: 51.091  
  --> Root in between the borders! Added to results.
  Coefficients: -43.9506666666667Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
  [20250402_172140.]: Samplename: 62.5  
  Root: 65.785  
  --> Root in between the borders! Added to results.
  Coefficients: -53.632Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
  [20250402_172140.]: Samplename: 75  
  Root: 75.683  
  --> Root in between the borders! Added to results.
  Coefficients: -61.6601481481482Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
  [20250402_172140.]: Samplename: 87.5  
  Root: 82.966  
  --> Root in between the borders! Added to results.
  Coefficients: -83.9631111111111Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
  [20250402_172140.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172140.]: ### Starting with regression calculations ###
  [20250402_172140.]: Entered 'regression_type1'-Function
  [20250402_172141.]: # CpG-site: CpG#1
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
  [20250402_172142.]: Logging df_agg: CpG#1
  [20250402_172142.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172142.]: c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
  [20250402_172142.]: Entered 'hyperbolic_regression'-Function
  [20250402_172142.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172142.]: Entered 'cubic_regression'-Function
  [20250402_172142.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172142.]: # CpG-site: CpG#2
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
  [20250402_172142.]: Logging df_agg: CpG#2
  [20250402_172142.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172142.]: c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
  [20250402_172142.]: Entered 'hyperbolic_regression'-Function
  [20250402_172142.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172143.]: Entered 'cubic_regression'-Function
  [20250402_172143.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172143.]: # CpG-site: CpG#3
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
  [20250402_172143.]: Logging df_agg: CpG#3
  [20250402_172143.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172143.]: c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
  [20250402_172143.]: Entered 'hyperbolic_regression'-Function
  [20250402_172143.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172143.]: Entered 'cubic_regression'-Function
  [20250402_172143.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172143.]: # CpG-site: CpG#4
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
  [20250402_172143.]: Logging df_agg: CpG#4
  [20250402_172143.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172143.]: c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
  [20250402_172143.]: Entered 'hyperbolic_regression'-Function
  [20250402_172143.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172143.]: Entered 'cubic_regression'-Function
  [20250402_172143.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172143.]: # CpG-site: CpG#5
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
  [20250402_172143.]: Logging df_agg: CpG#5
  [20250402_172143.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172143.]: c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
  [20250402_172143.]: Entered 'hyperbolic_regression'-Function
  [20250402_172143.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172144.]: Entered 'cubic_regression'-Function
  [20250402_172144.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172142.]: # CpG-site: CpG#6
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
  [20250402_172142.]: Logging df_agg: CpG#6
  [20250402_172142.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172142.]: c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
  [20250402_172142.]: Entered 'hyperbolic_regression'-Function
  [20250402_172142.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172142.]: Entered 'cubic_regression'-Function
  [20250402_172142.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172143.]: # CpG-site: CpG#7
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
  [20250402_172143.]: Logging df_agg: CpG#7
  [20250402_172143.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172143.]: c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
  [20250402_172143.]: Entered 'hyperbolic_regression'-Function
  [20250402_172143.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172143.]: Entered 'cubic_regression'-Function
  [20250402_172143.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172143.]: # CpG-site: CpG#8
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
  [20250402_172143.]: Logging df_agg: CpG#8
  [20250402_172143.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172143.]: c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
  [20250402_172143.]: Entered 'hyperbolic_regression'-Function
  [20250402_172143.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172143.]: Entered 'cubic_regression'-Function
  [20250402_172143.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172143.]: # CpG-site: CpG#9
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
  [20250402_172143.]: Logging df_agg: CpG#9
  [20250402_172143.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172143.]: c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
  [20250402_172143.]: Entered 'hyperbolic_regression'-Function
  [20250402_172143.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172143.]: Entered 'cubic_regression'-Function
  [20250402_172143.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172143.]: # CpG-site: row_means
  c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
  [20250402_172143.]: Logging df_agg: row_means
  [20250402_172143.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_172143.]: c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
  [20250402_172143.]: Entered 'hyperbolic_regression'-Function
  [20250402_172143.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172143.]: Entered 'cubic_regression'-Function
  [20250402_172143.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
  [20250402_172144.]: Entered 'solving_equations'-Function
  [20250402_172144.]: Solving hyperbolic regression for CpG#1
  Hyperbolic solved: 78.9856894800976
  [20250402_172144.]: Samplename: Sample#1  
  Root: 78.986  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 31.2695317984092
  [20250402_172144.]: Samplename: Sample#10  
  Root: 31.27  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 42.7015782380441
  [20250402_172145.]: Samplename: Sample#2  
  Root: 42.702  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 57.8152127901709
  [20250402_172145.]: Samplename: Sample#3  
  Root: 57.815  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 11.2334360674289
  [20250402_172145.]: Samplename: Sample#4  
  Root: 11.233  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 23.5293831001518
  [20250402_172145.]: Samplename: Sample#5  
  Root: 23.529  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 24.7706743072545
  [20250402_172145.]: Samplename: Sample#6  
  Root: 24.771  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 46.3953425213349
  [20250402_172145.]: Samplename: Sample#7  
  Root: 46.395  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 84.45071436915
  [20250402_172145.]: Samplename: Sample#8  
  Root: 84.451  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: -1.41337105576252
  [20250402_172145.]: Samplename: Sample#9  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -1.413  
  --> '-10 < root < 0' --> substitute 0
  [20250402_172145.]: Solving cubic regression for CpG#2
  Coefficients: -59.7333333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: Sample#1  
  Root: 76.346  
  --> Root in between the borders! Added to results.
  Coefficients: -19.048Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: Sample#10  
  Root: 31.371  
  --> Root in between the borders! Added to results.
  Coefficients: -27.8783333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: Sample#2  
  Root: 43.142  
  --> Root in between the borders! Added to results.
  Coefficients: -41.795Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: Sample#3  
  Root: 59.121  
  --> Root in between the borders! Added to results.
  Coefficients: -2.21Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: Sample#4  
  Root: 4.128  
  --> Root in between the borders! Added to results.
  Coefficients: -11.665Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: Sample#5  
  Root: 20.292  
  --> Root in between the borders! Added to results.
  Coefficients: -10.08Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: Sample#6  
  Root: 17.745  
  --> Root in between the borders! Added to results.
  Coefficients: -26.488Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: Sample#7  
  Root: 41.383  
  --> Root in between the borders! Added to results.
  Coefficients: -70.532Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: Sample#8  
  Root: 85.378  
  --> Root in between the borders! Added to results.
  Coefficients: -1.13Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: Sample#9  
  Root: 2.127  
  --> Root in between the borders! Added to results.
  [20250402_172145.]: Solving hyperbolic regression for CpG#3
  Hyperbolic solved: 74.5474014641742
  [20250402_172145.]: Samplename: Sample#1  
  Root: 74.547  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 28.3579002775045
  [20250402_172145.]: Samplename: Sample#10  
  Root: 28.358  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 42.6085496577593
  [20250402_172145.]: Samplename: Sample#2  
  Root: 42.609  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 56.3286114696456
  [20250402_172145.]: Samplename: Sample#3  
  Root: 56.329  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 7.99034441243248
  [20250402_172145.]: Samplename: Sample#4  
  Root: 7.99  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 24.7023143744962
  [20250402_172145.]: Samplename: Sample#5  
  Root: 24.702  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.8868798900698
  [20250402_172145.]: Samplename: Sample#6  
  Root: 26.887  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 44.8318233973603
  [20250402_172145.]: Samplename: Sample#7  
  Root: 44.832  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 84.6737871528405
  [20250402_172145.]: Samplename: Sample#8  
  Root: 84.674  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: -1.26200732612128
  [20250402_172145.]: Samplename: Sample#9  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -1.262  
  --> '-10 < root < 0' --> substitute 0
  [20250402_172145.]: Solving hyperbolic regression for CpG#4
  Hyperbolic solved: 75.8433680333876
  [20250402_172145.]: Samplename: Sample#1  
  Root: 75.843  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 29.0603248948201
  [20250402_172145.]: Samplename: Sample#10  
  Root: 29.06  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 44.0355928114108
  [20250402_172145.]: Samplename: Sample#2  
  Root: 44.036  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 58.7751115686327
  [20250402_172145.]: Samplename: Sample#3  
  Root: 58.775  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 11.0319154866029
  [20250402_172145.]: Samplename: Sample#4  
  Root: 11.032  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 22.9948971650737
  [20250402_172145.]: Samplename: Sample#5  
  Root: 22.995  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 27.9415139419957
  [20250402_172145.]: Samplename: Sample#6  
  Root: 27.942  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 42.4874049425657
  [20250402_172145.]: Samplename: Sample#7  
  Root: 42.487  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 84.6802730343613
  [20250402_172145.]: Samplename: Sample#8  
  Root: 84.68  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 3.00887785677921
  [20250402_172145.]: Samplename: Sample#9  
  Root: 3.009  
  --> Root in between the borders! Added to results.
  [20250402_172145.]: Solving cubic regression for CpG#5
  Coefficients: -47.8373333333333Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: Sample#1  
  Root: 72.291  
  --> Root in between the borders! Added to results.
  Coefficients: -13.588Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: Sample#10  
  Root: 27.212  
  --> Root in between the borders! Added to results.
  Coefficients: -25.3211428571429Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: Sample#2  
  Root: 44.85  
  --> Root in between the borders! Added to results.
  Coefficients: -32.064Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: Sample#3  
  Root: 53.741  
  --> Root in between the borders! Added to results.
  Coefficients: -4.074Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: Sample#4  
  Root: 9.444  
  --> Root in between the borders! Added to results.
  Coefficients: -11.434Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: Sample#5  
  Root: 23.55  
  --> Root in between the borders! Added to results.
  Coefficients: -13.294Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: Sample#6  
  Root: 26.722  
  --> Root in between the borders! Added to results.
  Coefficients: -24.288Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: Sample#7  
  Root: 43.42  
  --> Root in between the borders! Added to results.
  Coefficients: -63.134Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: Sample#8  
  Root: 88.215  
  --> Root in between the borders! Added to results.
  Coefficients: 0.0360000000000005Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: Sample#9  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -0.091  
  --> '-10 < root < 0' --> substitute 0
  [20250402_172145.]: Solving hyperbolic regression for CpG#6
  Hyperbolic solved: 79.2200555510382
  [20250402_172145.]: Samplename: Sample#1  
  Root: 79.22  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 30.2526528381147
  [20250402_172145.]: Samplename: Sample#10  
  Root: 30.253  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 41.9196854329573
  [20250402_172145.]: Samplename: Sample#2  
  Root: 41.92  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 56.8984354098215
  [20250402_172145.]: Samplename: Sample#3  
  Root: 56.898  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 8.81576403111374
  [20250402_172145.]: Samplename: Sample#4  
  Root: 8.816  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 18.6921622783918
  [20250402_172145.]: Samplename: Sample#5  
  Root: 18.692  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 29.9815019073132
  [20250402_172145.]: Samplename: Sample#6  
  Root: 29.982  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 42.8875178508205
  [20250402_172145.]: Samplename: Sample#7  
  Root: 42.888  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 86.6303733181195
  [20250402_172145.]: Samplename: Sample#8  
  Root: 86.63  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 1.38997712955107
  [20250402_172145.]: Samplename: Sample#9  
  Root: 1.39  
  --> Root in between the borders! Added to results.
  [20250402_172145.]: Solving hyperbolic regression for CpG#7
  Hyperbolic solved: 77.5278331978133
  [20250402_172145.]: Samplename: Sample#1  
  Root: 77.528  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 27.0895401031897
  [20250402_172145.]: Samplename: Sample#10  
  Root: 27.09  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 48.4382794903846
  [20250402_172145.]: Samplename: Sample#2  
  Root: 48.438  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 58.8815971416453
  [20250402_172145.]: Samplename: Sample#3  
  Root: 58.882  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 13.3295768294236
  [20250402_172145.]: Samplename: Sample#4  
  Root: 13.33  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.9816196357542
  [20250402_172145.]: Samplename: Sample#5  
  Root: 26.982  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 30.9612159665911
  [20250402_172145.]: Samplename: Sample#6  
  Root: 30.961  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 45.7456547820365
  [20250402_172145.]: Samplename: Sample#7  
  Root: 45.746  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 84.6033538318025
  [20250402_172145.]: Samplename: Sample#8  
  Root: 84.603  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: -2.87380061592101
  [20250402_172145.]: Samplename: Sample#9  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -2.874  
  --> '-10 < root < 0' --> substitute 0
  [20250402_172145.]: Solving cubic regression for CpG#8
  Coefficients: -55.3573333333333Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: Sample#1  
  Root: 72.421  
  --> Root in between the borders! Added to results.
  Coefficients: -17.574Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: Sample#10  
  Root: 28.533  
  --> Root in between the borders! Added to results.
  Coefficients: -22.9425714285714Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: Sample#2  
  Root: 35.766  
  --> Root in between the borders! Added to results.
  Coefficients: -42.849Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: Sample#3  
  Root: 59.36  
  --> Root in between the borders! Added to results.
  Coefficients: -4.604Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: Sample#4  
  Root: 8.481  
  --> Root in between the borders! Added to results.
  Coefficients: -11.389Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: Sample#5  
  Root: 19.519  
  --> Root in between the borders! Added to results.
  Coefficients: -25.784Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: Sample#6  
  Root: 39.413  
  --> Root in between the borders! Added to results.
  Coefficients: -30.746Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: Sample#7  
  Root: 45.53  
  --> Root in between the borders! Added to results.
  Coefficients: -66.912Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: Sample#8  
  Root: 83.654  
  --> Root in between the borders! Added to results.
  Coefficients: 3.176Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: Sample#9  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -6.535  
  --> '-10 < root < 0' --> substitute 0
  [20250402_172145.]: Solving hyperbolic regression for CpG#9
  Hyperbolic solved: 80.5486410672961
  [20250402_172145.]: Samplename: Sample#1  
  Root: 80.549  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 27.810468482135
  [20250402_172145.]: Samplename: Sample#10  
  Root: 27.81  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 46.2641649294309
  [20250402_172145.]: Samplename: Sample#2  
  Root: 46.264  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 57.1903653427228
  [20250402_172145.]: Samplename: Sample#3  
  Root: 57.19  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 8.63886339746086
  [20250402_172145.]: Samplename: Sample#4  
  Root: 8.639  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 24.2162393845509
  [20250402_172145.]: Samplename: Sample#5  
  Root: 24.216  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 39.6394430638471
  [20250402_172145.]: Samplename: Sample#6  
  Root: 39.639  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 44.3080887012493
  [20250402_172145.]: Samplename: Sample#7  
  Root: 44.308  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 87.3259098830063
  [20250402_172145.]: Samplename: Sample#8  
  Root: 87.326  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: -1.17959639730045
  [20250402_172145.]: Samplename: Sample#9  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -1.18  
  --> '-10 < root < 0' --> substitute 0
  [20250402_172145.]: Solving hyperbolic regression for row_means
  Hyperbolic solved: 76.7568961192102
  [20250402_172145.]: Samplename: Sample#1  
  Root: 76.757  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 28.8326630603664
  [20250402_172145.]: Samplename: Sample#10  
  Root: 28.833  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 43.0145327025204
  [20250402_172145.]: Samplename: Sample#2  
  Root: 43.015  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 57.6144798147902
  [20250402_172145.]: Samplename: Sample#3  
  Root: 57.614  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 8.86517972238162
  [20250402_172145.]: Samplename: Sample#4  
  Root: 8.865  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 22.1849817550475
  [20250402_172145.]: Samplename: Sample#5  
  Root: 22.185  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 29.1973843238972
  [20250402_172145.]: Samplename: Sample#6  
  Root: 29.197  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 43.9174258632975
  [20250402_172145.]: Samplename: Sample#7  
  Root: 43.917  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 85.6607695784409
  [20250402_172145.]: Samplename: Sample#8  
  Root: 85.661  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: -0.551158207550385
  [20250402_172145.]: Samplename: Sample#9  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Negative numeric root found:  
  Root: -0.551  
  --> '-10 < root < 0' --> substitute 0
  [20250402_172145.]: Entered 'solving_equations'-Function
  [20250402_172145.]: Solving hyperbolic regression for CpG#1
  Hyperbolic solved: 0
  [20250402_172145.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 14.1381159662486
  [20250402_172145.]: Samplename: 12.5  
  Root: 14.138  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.1241053609707
  [20250402_172145.]: Samplename: 25  
  Root: 26.124  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 39.3567419170867
  [20250402_172145.]: Samplename: 37.5  
  Root: 39.357  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 52.9273107806133
  [20250402_172145.]: Samplename: 50  
  Root: 52.927  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 65.4010628999278
  [20250402_172145.]: Samplename: 62.5  
  Root: 65.401  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 74.4183184249663
  [20250402_172145.]: Samplename: 75  
  Root: 74.418  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 80.5431520527512
  [20250402_172145.]: Samplename: 87.5  
  Root: 80.543  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172145.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172145.]: Solving cubic regression for CpG#2
  Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: 12.5  
  Root: 10.991  
  --> Root in between the borders! Added to results.
  Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: 25  
  Root: 26.435  
  --> Root in between the borders! Added to results.
  Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: 37.5  
  Root: 35.545  
  --> Root in between the borders! Added to results.
  Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: 50  
  Root: 48.102  
  --> Root in between the borders! Added to results.
  Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: 62.5  
  Root: 67.086  
  --> Root in between the borders! Added to results.
  Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: 75  
  Root: 75.419  
  --> Root in between the borders! Added to results.
  Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: 87.5  
  Root: 83.785  
  --> Root in between the borders! Added to results.
  Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
  [20250402_172145.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172145.]: Solving hyperbolic regression for CpG#3
  Hyperbolic solved: 0
  [20250402_172145.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 10.8497990553835
  [20250402_172145.]: Samplename: 12.5  
  Root: 10.85  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.1511183533449
  [20250402_172145.]: Samplename: 25  
  Root: 26.151  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 37.2940213300522
  [20250402_172145.]: Samplename: 37.5  
  Root: 37.294  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 51.419361136507
  [20250402_172145.]: Samplename: 50  
  Root: 51.419  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 65.0212050873619
  [20250402_172145.]: Samplename: 62.5  
  Root: 65.021  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 76.9977789568509
  [20250402_172145.]: Samplename: 75  
  Root: 76.998  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 79.686036177122
  [20250402_172145.]: Samplename: 87.5  
  Root: 79.686  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172145.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172145.]: Solving hyperbolic regression for CpG#4
  Hyperbolic solved: 0
  [20250402_172145.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 13.2434477796981
  [20250402_172145.]: Samplename: 12.5  
  Root: 13.243  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 25.0815867666892
  [20250402_172145.]: Samplename: 25  
  Root: 25.082  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 38.7956859187734
  [20250402_172145.]: Samplename: 37.5  
  Root: 38.796  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 49.1001600195185
  [20250402_172145.]: Samplename: 50  
  Root: 49.1  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 67.5620415214226
  [20250402_172145.]: Samplename: 62.5  
  Root: 67.562  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 73.7554076043322
  [20250402_172145.]: Samplename: 75  
  Root: 73.755  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 82.0327440839301
  [20250402_172145.]: Samplename: 87.5  
  Root: 82.033  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172145.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172145.]: Solving cubic regression for CpG#5
  Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: 12.5  
  Root: 9.593  
  --> Root in between the borders! Added to results.
  Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: 25  
  Root: 24.704  
  --> Root in between the borders! Added to results.
  Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: 37.5  
  Root: 38.051  
  --> Root in between the borders! Added to results.
  Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: 50  
  Root: 51.187  
  --> Root in between the borders! Added to results.
  Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: 62.5  
  Root: 62.269  
  --> Root in between the borders! Added to results.
  Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: 75  
  Root: 75.786  
  --> Root in between the borders! Added to results.
  Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: 87.5  
  Root: 85.475  
  --> Root in between the borders! Added to results.
  Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
  [20250402_172145.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 100  
  --> '100 < root < 110' --> substitute 100
  [20250402_172145.]: Solving hyperbolic regression for CpG#6
  Hyperbolic solved: 0
  [20250402_172145.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 11.822687731114
  [20250402_172145.]: Samplename: 12.5  
  Root: 11.823  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.5494368772504
  [20250402_172145.]: Samplename: 25  
  Root: 26.549  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 35.3846787677878
  [20250402_172145.]: Samplename: 37.5  
  Root: 35.385  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 50.1264563333089
  [20250402_172145.]: Samplename: 50  
  Root: 50.126  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 64.9875101866844
  [20250402_172145.]: Samplename: 62.5  
  Root: 64.988  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 73.6494948240195
  [20250402_172145.]: Samplename: 75  
  Root: 73.649  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 87.0033714659226
  [20250402_172145.]: Samplename: 87.5  
  Root: 87.003  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172145.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172145.]: Solving hyperbolic regression for CpG#7
  Hyperbolic solved: 0
  [20250402_172145.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 11.7925453863418
  [20250402_172145.]: Samplename: 12.5  
  Root: 11.793  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 26.2042827174053
  [20250402_172145.]: Samplename: 25  
  Root: 26.204  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 39.2081609373531
  [20250402_172145.]: Samplename: 37.5  
  Root: 39.208  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 54.3620766326312
  [20250402_172145.]: Samplename: 50  
  Root: 54.362  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 66.0664882334621
  [20250402_172145.]: Samplename: 62.5  
  Root: 66.066  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 75.1981507250883
  [20250402_172145.]: Samplename: 75  
  Root: 75.198  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 78.6124357632637
  [20250402_172145.]: Samplename: 87.5  
  Root: 78.612  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172145.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172145.]: Solving cubic regression for CpG#8
  Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: 12.5  
  Root: 8.039  
  --> Root in between the borders! Added to results.
  Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: 25  
  Root: 26.079  
  --> Root in between the borders! Added to results.
  Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: 37.5  
  Root: 34.864  
  --> Root in between the borders! Added to results.
  Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: 50  
  Root: 52.311  
  --> Root in between the borders! Added to results.
  Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: 62.5  
  Root: 64.584  
  --> Root in between the borders! Added to results.
  Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: 75  
  Root: 77.576  
  --> Root in between the borders! Added to results.
  Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: 87.5  
  Root: 80.326  
  --> Root in between the borders! Added to results.
  Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
  [20250402_172145.]: Samplename: 100  
    
  ## WARNING ##
  No fitting root within the borders found.  
  Positive numeric root found:  
  Root: 100  
  --> '100 < root < 110' --> substitute 100
  [20250402_172145.]: Solving hyperbolic regression for CpG#9
  Hyperbolic solved: 0
  [20250402_172145.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 12.2094906593745
  [20250402_172145.]: Samplename: 12.5  
  Root: 12.209  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 28.0738986154201
  [20250402_172145.]: Samplename: 25  
  Root: 28.074  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 37.6720254587223
  [20250402_172145.]: Samplename: 37.5  
  Root: 37.672  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 52.3746308870569
  [20250402_172145.]: Samplename: 50  
  Root: 52.375  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 64.8693631845077
  [20250402_172145.]: Samplename: 62.5  
  Root: 64.869  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 74.2598902601534
  [20250402_172145.]: Samplename: 75  
  Root: 74.26  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 83.9376844048195
  [20250402_172145.]: Samplename: 87.5  
  Root: 83.938  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172145.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  [20250402_172145.]: Solving hyperbolic regression for row_means
  Hyperbolic solved: 0
  [20250402_172145.]: Samplename: 0  
  Root: 0  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 11.1506882890389
  [20250402_172145.]: Samplename: 12.5  
  Root: 11.151  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 25.841636381907
  [20250402_172145.]: Samplename: 25  
  Root: 25.842  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 37.0462679509085
  [20250402_172145.]: Samplename: 37.5  
  Root: 37.046  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 51.1681297765954
  [20250402_172145.]: Samplename: 50  
  Root: 51.168  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 65.4258217891781
  [20250402_172145.]: Samplename: 62.5  
  Root: 65.426  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 75.285632789037
  [20250402_172145.]: Samplename: 75  
  Root: 75.286  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 82.6475419323379
  [20250402_172145.]: Samplename: 87.5  
  Root: 82.648  
  --> Root in between the borders! Added to results.
  Hyperbolic solved: 100
  [20250402_172145.]: Samplename: 100  
  Root: 100  
  --> Root in between the borders! Added to results.
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  [20250402_172224.]: Entered 'clean_dt'-Function
  [20250402_172224.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
  [20250402_172224.]: got experimental data
  [20250402_172224.]: Entered 'clean_dt'-Function
  [20250402_172224.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data)
  [20250402_172224.]: got experimental data
  [20250402_172224.]: Entered 'clean_dt'-Function
  [20250402_172224.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
  [20250402_172224.]: got calibration data
  [20250402_172224.]: Entered 'clean_dt'-Function
  [20250402_172224.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
  [20250402_172224.]: got calibration data
  [20250402_172224.]: Entered 'hyperbolic_regression'-Function
  [20250402_172224.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    step factor 0.000488281 reduced below 'minFactor' of 0.000976562
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  Error in (function (formula, data = parent.frame(), start, control = nls.control(),  : 
    singular gradient
  [ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
  
  ══ Skipped tests (4) ═══════════════════════════════════════════════════════════
  • On CRAN (4): 'test-algorithm_minmax_FALSE.R:80:5',
    'test-algorithm_minmax_TRUE.R:76:5', 'test-hyperbolic.R:27:5',
    'test-lints.R:12:5'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-algorithm_minmax_FALSE_re.R:170:5'): algorithm test, type 1, minmax = FALSE selection_method = RelError ──
  Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
  Backtrace:
       ▆
    1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_FALSE_re.R:170:5
    2.   ├─testthat:::check_roundtrip(...)
    3.   │ └─testthat:::waldo_compare(...)
    4.   │   └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
    5.   │     └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
    6.   │       └─rlang::is_missing(y)
    7.   └─testthat (local) load(save(x))
    8.     └─jsonlite::unserializeJSON(x)
    9.       └─jsonlite:::unpack(parseJSON(txt))
   10.         └─base::lapply(obj$attributes, unpack)
   11.           └─jsonlite (local) FUN(X[[i]], ...)
   12.             ├─base::do.call("structure", newdata, quote = TRUE)
   13.             └─base::structure(.Data = base::quote(NULL))
  ── Error ('test-algorithm_minmax_TRUE_re.R:170:5'): algorithm test, type 1, minmax = TRUE selection_method = RelError ──
  Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
  Backtrace:
       ▆
    1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_TRUE_re.R:170:5
    2.   ├─testthat:::check_roundtrip(...)
    3.   │ └─testthat:::waldo_compare(...)
    4.   │   └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
    5.   │     └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
    6.   │       └─rlang::is_missing(y)
    7.   └─testthat (local) load(save(x))
    8.     └─jsonlite::unserializeJSON(x)
    9.       └─jsonlite:::unpack(parseJSON(txt))
   10.         └─base::lapply(obj$attributes, unpack)
   11.           └─jsonlite (local) FUN(X[[i]], ...)
   12.             ├─base::do.call("structure", newdata, quote = TRUE)
   13.             └─base::structure(.Data = base::quote(NULL))
  ── Error ('test-clean_dt.R:17:5'): test normal function of file import of type 1 ──
  Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
  Backtrace:
       ▆
    1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:17:5
    2.   ├─testthat:::check_roundtrip(...)
    3.   │ └─testthat:::waldo_compare(...)
    4.   │   └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
    5.   │     └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
    6.   │       └─rlang::is_missing(y)
    7.   └─testthat (local) load(save(x))
    8.     └─jsonlite::unserializeJSON(x)
    9.       └─jsonlite:::unpack(parseJSON(txt))
   10.         └─base::lapply(obj$attributes, unpack)
   11.           └─jsonlite (local) FUN(X[[i]], ...)
   12.             ├─base::do.call("structure", newdata, quote = TRUE)
   13.             └─base::structure(.Data = base::quote(NULL))
  ── Error ('test-clean_dt.R:65:5'): test normal function of file import of type 2 ──
  Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
  Backtrace:
       ▆
    1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:65:5
    2.   ├─testthat:::check_roundtrip(...)
    3.   │ └─testthat:::waldo_compare(...)
    4.   │   └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
    5.   │     └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
    6.   │       └─rlang::is_missing(y)
    7.   └─testthat (local) load(save(x))
    8.     └─jsonlite::unserializeJSON(x)
    9.       └─jsonlite:::unpack(parseJSON(txt))
   10.         └─base::lapply(obj$attributes, unpack)
   11.           └─jsonlite (local) FUN(X[[i]], ...)
   12.             ├─base::do.call("structure", newdata, quote = TRUE)
   13.             └─base::structure(.Data = base::quote(NULL))
  ── Error ('test-create_aggregated.R:19:5'): test functioning of aggregated function ──
  Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
  Backtrace:
       ▆
    1. └─testthat::expect_snapshot_value(...) at test-create_aggregated.R:19:5
    2.   ├─testthat:::check_roundtrip(...)
    3.   │ └─testthat:::waldo_compare(...)
    4.   │   └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
    5.   │     └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
    6.   │       └─rlang::is_missing(y)
    7.   └─testthat (local) load(save(x))
    8.     └─jsonlite::unserializeJSON(x)
    9.       └─jsonlite:::unpack(parseJSON(txt))
   10.         └─base::lapply(obj$attributes, unpack)
   11.           └─jsonlite (local) FUN(X[[i]], ...)
   12.             ├─base::do.call("structure", newdata, quote = TRUE)
   13.             └─base::structure(.Data = base::quote(NULL))
  
  [ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
  Error: Test failures
  Execution halted
  Error in deferred_run(env) : could not find function "deferred_run"
  Calls: <Anonymous>