Running ‘testthat.R’ [487s/468s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > # Part of the rstanarm package for estimating model parameters
  > # Copyright (C) 2015 Trustees of Columbia University
  > # 
  > # This program is free software; you can redistribute it and/or
  > # modify it under the terms of the GNU General Public License
  > # as published by the Free Software Foundation; either version 3
  > # of the License, or (at your option) any later version.
  > # 
  > # This program is distributed in the hope that it will be useful,
  > # but WITHOUT ANY WARRANTY; without even the implied warranty of
  > # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  > # GNU General Public License for more details.
  > # 
  > # You should have received a copy of the GNU General Public License
  > # along with this program; if not, write to the Free Software
  > # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
  > 
  > library(testthat)
  > library(rstanarm)
  Loading required package: Rcpp
  rstanarm (Version 2.17.4, packaged: 2018-04-13 01:51:52 UTC)
  - Do not expect the default priors to remain the same in future rstanarm versions.
  Thus, R scripts should specify priors explicitly, even if they are just the defaults.
  - For execution on a local, multicore CPU with excess RAM we recommend calling
  options(mc.cores = parallel::detectCores())
  - Plotting theme set to bayesplot::theme_default().
  > Sys.unsetenv("R_TESTS")
  > # options(error = function() traceback(2))
  > example(example_model)
  
  exmpl_> example_model <- 
  exmpl_+   stan_glmer(cbind(incidence, size - incidence) ~ size + period + (1|herd),
  exmpl_+              data = lme4::cbpp, family = binomial, QR = TRUE,
  exmpl_+              # this next line is only to keep the example small in size!
  exmpl_+              chains = 2, cores = 1, seed = 12345, iter = 500)
  
  SAMPLING FOR MODEL 'binomial' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 500 [  0%]  (Warmup)
  Iteration:  50 / 500 [ 10%]  (Warmup)
  Iteration: 100 / 500 [ 20%]  (Warmup)
  Iteration: 150 / 500 [ 30%]  (Warmup)
  Iteration: 200 / 500 [ 40%]  (Warmup)
  Iteration: 250 / 500 [ 50%]  (Warmup)
  Iteration: 251 / 500 [ 50%]  (Sampling)
  Iteration: 300 / 500 [ 60%]  (Sampling)
  Iteration: 350 / 500 [ 70%]  (Sampling)
  Iteration: 400 / 500 [ 80%]  (Sampling)
  Iteration: 450 / 500 [ 90%]  (Sampling)
  Iteration: 500 / 500 [100%]  (Sampling)
  
   Elapsed Time: 1.54 seconds (Warm-up)
                 0.64 seconds (Sampling)
                 2.18 seconds (Total)
  
  
  SAMPLING FOR MODEL 'binomial' NOW (CHAIN 2).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 500 [  0%]  (Warmup)
  Iteration:  50 / 500 [ 10%]  (Warmup)
  Iteration: 100 / 500 [ 20%]  (Warmup)
  Iteration: 150 / 500 [ 30%]  (Warmup)
  Iteration: 200 / 500 [ 40%]  (Warmup)
  Iteration: 250 / 500 [ 50%]  (Warmup)
  Iteration: 251 / 500 [ 50%]  (Sampling)
  Iteration: 300 / 500 [ 60%]  (Sampling)
  Iteration: 350 / 500 [ 70%]  (Sampling)
  Iteration: 400 / 500 [ 80%]  (Sampling)
  Iteration: 450 / 500 [ 90%]  (Sampling)
  Iteration: 500 / 500 [100%]  (Sampling)
  
   Elapsed Time: 1.95 seconds (Warm-up)
                 0.55 seconds (Sampling)
                 2.5 seconds (Total)
  
  
  exmpl_> example_model
  stan_glmer
   family:       binomial [logit]
   formula:      cbind(incidence, size - incidence) ~ size + period + (1 | herd)
   observations: 56
  ------
              Median MAD_SD
  (Intercept) -1.5    0.7  
  size         0.0    0.0  
  period2     -1.0    0.3  
  period3     -1.1    0.3  
  period4     -1.6    0.5  
  
  Error terms:
   Groups Name        Std.Dev.
   herd   (Intercept) 0.76    
  Num. levels: herd 15 
  
  Sample avg. posterior predictive distribution of y:
           Median MAD_SD
  mean_PPD 1.8    0.2   
  
  ------
  For info on the priors used see help('prior_summary.stanreg').
  > if (!grepl("^sparc",  R.version$platform)) 
  +   test_check("rstanarm", invert = TRUE,
  +              filter = if (Sys.getenv("NOT_CRAN") != "true") "jm|mvmer")
  Initial log joint probability = -3395
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  ------------------------------------------------------------
  EXPERIMENTAL ALGORITHM:
    This procedure has not been thoroughly tested and may be unstable
    or buggy. The interface is subject to change.
  ------------------------------------------------------------
  
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Begin eta adaptation.
  Iteration:   1 / 250 [  0%]  (Adaptation)
  Iteration:  50 / 250 [ 20%]  (Adaptation)
  Iteration: 100 / 250 [ 40%]  (Adaptation)
  Iteration: 150 / 250 [ 60%]  (Adaptation)
  Iteration: 200 / 250 [ 80%]  (Adaptation)
  Success! Found best value [eta = 1] earlier than expected.
  
  Begin stochastic gradient ascent.
    iter       ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
  Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
  This variational approximation is not guaranteed to be meaningful.
  
  Drawing a sample of size 1000 from the approximate posterior... 
  COMPLETED.
  ------------------------------------------------------------
  EXPERIMENTAL ALGORITHM:
    This procedure has not been thoroughly tested and may be unstable
    or buggy. The interface is subject to change.
  ------------------------------------------------------------
  
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Begin eta adaptation.
  Iteration:   1 / 250 [  0%]  (Adaptation)
  Iteration:  50 / 250 [ 20%]  (Adaptation)
  Iteration: 100 / 250 [ 40%]  (Adaptation)
  Iteration: 150 / 250 [ 60%]  (Adaptation)
  Success! Found best value [eta = 10] earlier than expected.
  
  Begin stochastic gradient ascent.
    iter       ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
  Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
  This variational approximation is not guaranteed to be meaningful.
  
  Drawing a sample of size 1000 from the approximate posterior... 
  COMPLETED.
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 3
             adapt_window = 20
             term_buffer = 2
  
  
   Elapsed Time: 0.02 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.03 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 3
             adapt_window = 20
             term_buffer = 2
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0.04 seconds (Sampling)
                 0.05 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 3
             adapt_window = 20
             term_buffer = 2
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.02 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 3
             adapt_window = 20
             term_buffer = 2
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0.06 seconds (Sampling)
                 0.07 seconds (Total)
  
  ── 1. Error: loo issues errors/warnings (@test_loo.R#90)  ──────────────────────
  subscript out of bounds
  1: expect_warning(loo(example_model, cores = LOO.CORES, k_threshold = 2), "Setting 'k_threshold' > 1 is not recommended") at testthat/test_loo.R:90
  2: quasi_capture(enquo(object), capture_warnings, label = label)
  3: capture(act$val <- eval_bare(get_expr(quo), get_env(quo)))
  4: withCallingHandlers(code, warning = function(condition) {
         out$push(condition)
         invokeRestart("muffleWarning")
     })
  5: eval_bare(get_expr(quo), get_env(quo))
  6: loo(example_model, cores = LOO.CORES, k_threshold = 2)
  7: loo.stanreg(example_model, cores = LOO.CORES, k_threshold = 2)
  8: suppressWarnings(loo.function(llfun, data = args$data, draws = args$draws, r_eff = r_eff, 
         ..., cores = cores, save_psis = save_psis))
  9: withCallingHandlers(expr, warning = function(w) invokeRestart("muffleWarning"))
  10: loo.function(llfun, data = args$data, draws = args$draws, r_eff = r_eff, ..., cores = cores, 
         save_psis = save_psis)
  11: lapply(psis_list, "[[", "pointwise")
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 14
             adapt_window = 76
             term_buffer = 10
  
  
   Elapsed Time: 0.05 seconds (Warm-up)
                 0.03 seconds (Sampling)
                 0.08 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 14
             adapt_window = 76
             term_buffer = 10
  
  
   Elapsed Time: 0.03 seconds (Warm-up)
                 0.03 seconds (Sampling)
                 0.06 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 14
             adapt_window = 76
             term_buffer = 10
  
  
   Elapsed Time: 0.03 seconds (Warm-up)
                 0.04 seconds (Sampling)
                 0.07 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 14
             adapt_window = 76
             term_buffer = 10
  
  
   Elapsed Time: 0.02 seconds (Warm-up)
                 0.04 seconds (Sampling)
                 0.06 seconds (Total)
  
  Initial log joint probability = -306.809
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.02 seconds (Warm-up)
                 0.06 seconds (Sampling)
                 0.08 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.02 seconds (Total)
  
  
   4-fold cross-validation
  
             Estimate  SE
  elpd_kfold    -82.6 4.6
  
   2-fold cross-validation
  
             Estimate  SE
  elpd_kfold   -100.6 9.4
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.01 seconds (Total)
  
  Error in new_CppObject_xp(fields$.module, fields$.pointer, ...) : 
    object 'SEED' not found
  Initial log joint probability = -25083.8
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  ------------------------------------------------------------
  EXPERIMENTAL ALGORITHM:
    This procedure has not been thoroughly tested and may be unstable
    or buggy. The interface is subject to change.
  ------------------------------------------------------------
  
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Begin eta adaptation.
  Iteration:   1 / 250 [  0%]  (Adaptation)
  Iteration:  50 / 250 [ 20%]  (Adaptation)
  Iteration: 100 / 250 [ 40%]  (Adaptation)
  Iteration: 150 / 250 [ 60%]  (Adaptation)
  Iteration: 200 / 250 [ 80%]  (Adaptation)
  Success! Found best value [eta = 1] earlier than expected.
  
  Begin stochastic gradient ascent.
    iter       ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
     100     -3e+02             1.000            1.000
     200     -2e+02             0.977            1.000
     300     -1e+02             0.680            0.955
     400     -1e+02             0.517            0.955
     500     -1e+02             0.429            0.087
     600     -1e+02             0.370            0.087
     700     -1e+02             0.325            0.080
     800     -1e+02             0.290            0.080
     900     -1e+02             0.260            0.071
    1000     -1e+02             0.238            0.071
    1100     -1e+02             0.138            0.060
    1200     -1e+02             0.042            0.039
    1300     -1e+02             0.035            0.035
    1400     -1e+02             0.039            0.039
    1500     -1e+02             0.036            0.039
    1600     -1e+02             0.030            0.035
    1700     -1e+02             0.027            0.031
    1800     -1e+02             0.024            0.024
    1900     -1e+02             0.022            0.015
    2000     -1e+02             0.021            0.015
    2100     -1e+02             0.021            0.015
    2200     -1e+02             0.025            0.022
    2300     -1e+02             0.024            0.022
    2400     -1e+02             0.018            0.014
    2500     -1e+02             0.015            0.014
    2600     -1e+02             0.015            0.015
    2700     -1e+02             0.012            0.010
    2800     -1e+02             0.015            0.015
    2900     -1e+02             0.019            0.018
    3000     -1e+02             0.018            0.015
    3100     -1e+02             0.020            0.018
    3200     -1e+02             0.016            0.015
    3300     -1e+02             0.017            0.015
    3400     -1e+02             0.018            0.015
    3500     -1e+02             0.017            0.010
    3600     -1e+02             0.015            0.009   MEDIAN ELBO CONVERGED
  
  Drawing a sample of size 1000 from the approximate posterior... 
  COMPLETED.
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  Iteration:  1 / 100 [  1%]  (Warmup)
  Iteration: 10 / 100 [ 10%]  (Warmup)
  Iteration: 20 / 100 [ 20%]  (Warmup)
  Iteration: 30 / 100 [ 30%]  (Warmup)
  Iteration: 40 / 100 [ 40%]  (Warmup)
  Iteration: 50 / 100 [ 50%]  (Warmup)
  Iteration: 51 / 100 [ 51%]  (Sampling)
  Iteration: 60 / 100 [ 60%]  (Sampling)
  Iteration: 70 / 100 [ 70%]  (Sampling)
  Iteration: 80 / 100 [ 80%]  (Sampling)
  Iteration: 90 / 100 [ 90%]  (Sampling)
  Iteration: 100 / 100 [100%]  (Sampling)
  
   Elapsed Time: 0.03 seconds (Warm-up)
                 0.03 seconds (Sampling)
                 0.06 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  
   Elapsed Time: 0.28 seconds (Warm-up)
                 0.18 seconds (Sampling)
                 0.46 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  
   Elapsed Time: 0.27 seconds (Warm-up)
                 0.19 seconds (Sampling)
                 0.46 seconds (Total)
  
  Initial log joint probability = -470.264
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  ------------------------------------------------------------
  EXPERIMENTAL ALGORITHM:
    This procedure has not been thoroughly tested and may be unstable
    or buggy. The interface is subject to change.
  ------------------------------------------------------------
  
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Begin eta adaptation.
  Iteration:   1 / 250 [  0%]  (Adaptation)
  Iteration:  50 / 250 [ 20%]  (Adaptation)
  Iteration: 100 / 250 [ 40%]  (Adaptation)
  Iteration: 150 / 250 [ 60%]  (Adaptation)
  Iteration: 200 / 250 [ 80%]  (Adaptation)
  Success! Found best value [eta = 1] earlier than expected.
  
  Begin stochastic gradient ascent.
    iter       ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
     100     -1e+02             1.000            1.000
     200     -1e+02             0.549            1.000
     300     -1e+02             0.421            0.164
     400     -1e+02             0.365            0.197
     500     -9e+01             0.299            0.164
     600     -9e+01             0.250            0.164
     700     -9e+01             0.215            0.097
     800     -9e+01             0.189            0.097
     900     -9e+01             0.168            0.038
    1000     -1e+02             0.157            0.058
    1100     -9e+01             0.063            0.058
    1200     -9e+01             0.054            0.038
    1300     -9e+01             0.038            0.012
    1400     -9e+01             0.020            0.012
    1500     -9e+01             0.018            0.012
    1600     -9e+01             0.018            0.012
    1700     -9e+01             0.020            0.012
    1800     -9e+01             0.019            0.012
    1900     -9e+01             0.020            0.012
    2000     -9e+01             0.014            0.012
    2100     -9e+01             0.008            0.009   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
  
  Drawing a sample of size 1000 from the approximate posterior... 
  COMPLETED.
  ------------------------------------------------------------
  EXPERIMENTAL ALGORITHM:
    This procedure has not been thoroughly tested and may be unstable
    or buggy. The interface is subject to change.
  ------------------------------------------------------------
  
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Begin eta adaptation.
  Iteration:   1 / 250 [  0%]  (Adaptation)
  Iteration:  50 / 250 [ 20%]  (Adaptation)
  Iteration: 100 / 250 [ 40%]  (Adaptation)
  Iteration: 150 / 250 [ 60%]  (Adaptation)
  Iteration: 200 / 250 [ 80%]  (Adaptation)
  Success! Found best value [eta = 1] earlier than expected.
  
  Begin stochastic gradient ascent.
    iter       ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
     100     -4e+02             1.000            1.000
     200     -2e+02             1.286            1.573
     300     -1e+02             0.937            1.000
     400     -1e+02             0.721            1.000
     500     -1e+02             0.577            0.237
     600     -1e+02             0.497            0.237
     700     -1e+02             0.448            0.154
     800     -1e+02             0.394            0.154
     900     -1e+02             0.355            0.093
    1000     -1e+02             0.328            0.093
    1100     -1e+02             0.228            0.081
    1200     -1e+02             0.072            0.073
    1300     -9e+01             0.051            0.047
    1400     -9e+01             0.045            0.031
    1500     -1e+02             0.051            0.047
    1600     -9e+01             0.049            0.047
    1700     -9e+01             0.034            0.031
    1800     -1e+02             0.034            0.031
    1900     -9e+01             0.034            0.031
    2000     -1e+02             0.031            0.031
    2100     -9e+01             0.035            0.037
    2200     -9e+01             0.036            0.037
    2300     -1e+02             0.041            0.042
    2400     -9e+01             0.049            0.057
    2500     -9e+01             0.043            0.042
    2600     -9e+01             0.037            0.037
    2700     -9e+01             0.037            0.037
    2800     -1e+02             0.041            0.042
    2900     -9e+01             0.042            0.052
    3000     -1e+02             0.039            0.037
    3100     -9e+01             0.036            0.024
    3200     -9e+01             0.036            0.024
    3300     -9e+01             0.027            0.014
    3400     -9e+01             0.018            0.011
    3500     -1e+02             0.022            0.014
    3600     -9e+01             0.024            0.024
    3700     -9e+01             0.026            0.024
    3800     -9e+01             0.020            0.016
    3900     -9e+01             0.016            0.014
    4000     -1e+02             0.018            0.014
    4100     -9e+01             0.020            0.016
    4200     -9e+01             0.020            0.017
    4300     -9e+01             0.021            0.017
    4400     -9e+01             0.023            0.018
    4500     -9e+01             0.022            0.018
    4600     -9e+01             0.022            0.018
    4700     -9e+01             0.023            0.024
    4800     -9e+01             0.023            0.024
    4900     -9e+01             0.022            0.024
    5000     -9e+01             0.019            0.018
    5100     -9e+01             0.019            0.018
    5200     -9e+01             0.018            0.018
    5300     -9e+01             0.018            0.018
    5400     -9e+01             0.016            0.013
    5500     -9e+01             0.014            0.013
    5600     -9e+01             0.012            0.008   MEDIAN ELBO CONVERGED
  
  Drawing a sample of size 1000 from the approximate posterior... 
  COMPLETED.
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  
   Elapsed Time: 1.34 seconds (Warm-up)
                 0.9 seconds (Sampling)
                 2.24 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  
   Elapsed Time: 2.44 seconds (Warm-up)
                 0.93 seconds (Sampling)
                 3.37 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  
   Elapsed Time: 0.03 seconds (Warm-up)
                 0.03 seconds (Sampling)
                 0.06 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  
   Elapsed Time: 0.03 seconds (Warm-up)
                 0.02 seconds (Sampling)
                 0.05 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0 seconds (Warm-up)
                 0 seconds (Sampling)
                 0 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0 seconds (Warm-up)
                 0 seconds (Sampling)
                 0 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0.02 seconds (Sampling)
                 0.03 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0.02 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.03 seconds (Total)
  
  ------------------------------------------------------------
  EXPERIMENTAL ALGORITHM:
    This procedure has not been thoroughly tested and may be unstable
    or buggy. The interface is subject to change.
  ------------------------------------------------------------
  
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Begin eta adaptation.
  Iteration:   1 / 250 [  0%]  (Adaptation)
  Iteration:  50 / 250 [ 20%]  (Adaptation)
  Iteration: 100 / 250 [ 40%]  (Adaptation)
  Iteration: 150 / 250 [ 60%]  (Adaptation)
  Iteration: 200 / 250 [ 80%]  (Adaptation)
  Success! Found best value [eta = 1] earlier than expected.
  
  Begin stochastic gradient ascent.
    iter       ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
     100     -1e+02             1.000            1.000
     200     -1e+02             0.589            1.000
     300     -9e+01             0.467            0.221
     400     -9e+01             0.352            0.221
     500     -9e+01             0.282            0.178
     600     -9e+01             0.237            0.178
     700     -9e+01             0.204            0.012
     800     -9e+01             0.179            0.012
     900     -9e+01             0.159            0.009   MEDIAN ELBO CONVERGED
  
  Drawing a sample of size 1000 from the approximate posterior... 
  COMPLETED.
  Initial log joint probability = -470.264
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0 seconds (Warm-up)
                 0 seconds (Sampling)
                 0 seconds (Total)
  
  Initial log joint probability = -569.387
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -154193
  Exception: beta_lpdf: First shape parameter[4] is 0, but must be > 0!  (in 'model_continuous' at line 192)
  
  Exception: beta_lpdf: First shape parameter[4] is 0, but must be > 0!  (in 'model_continuous' at line 192)
  
  Exception: beta_lpdf: First shape parameter[14] is 0, but must be > 0!  (in 'model_continuous' at line 192)
  
  Exception: beta_lpdf: First shape parameter[47] is 0, but must be > 0!  (in 'model_continuous' at line 192)
  
  
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -491.938
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -879.578
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -484.693
  Exception: beta_lpdf: Second shape parameter[1] is 0, but must be > 0!  (in 'model_continuous' at line 185)
  
  
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -426.896
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -615.262
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Rejecting initial value:
    Error evaluating the log probability at the initial value.
  Exception: beta_lpdf: First shape parameter[6] is 0, but must be > 0!  (in 'model_continuous' at line 185)
  
  Rejecting initial value:
    Error evaluating the log probability at the initial value.
  Exception: beta_lpdf: First shape parameter[6] is 0, but must be > 0!  (in 'model_continuous' at line 185)
  
  Initial log joint probability = -257.044
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -569.239
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  Iteration:  1 / 100 [  1%]  (Warmup)
  Iteration: 10 / 100 [ 10%]  (Warmup)
  Iteration: 20 / 100 [ 20%]  (Warmup)
  Iteration: 30 / 100 [ 30%]  (Warmup)
  Iteration: 40 / 100 [ 40%]  (Warmup)
  Iteration: 50 / 100 [ 50%]  (Warmup)
  Iteration: 51 / 100 [ 51%]  (Sampling)
  Iteration: 60 / 100 [ 60%]  (Sampling)
  Iteration: 70 / 100 [ 70%]  (Sampling)
  Iteration: 80 / 100 [ 80%]  (Sampling)
  Iteration: 90 / 100 [ 90%]  (Sampling)
  Iteration: 100 / 100 [100%]  (Sampling)
  
   Elapsed Time: 0.18 seconds (Warm-up)
                 0.24 seconds (Sampling)
                 0.42 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  Iteration:  1 / 100 [  1%]  (Warmup)
  Iteration: 10 / 100 [ 10%]  (Warmup)
  Iteration: 20 / 100 [ 20%]  (Warmup)
  Iteration: 30 / 100 [ 30%]  (Warmup)
  Iteration: 40 / 100 [ 40%]  (Warmup)
  Iteration: 50 / 100 [ 50%]  (Warmup)
  Iteration: 51 / 100 [ 51%]  (Sampling)
  Iteration: 60 / 100 [ 60%]  (Sampling)
  Iteration: 70 / 100 [ 70%]  (Sampling)
  Iteration: 80 / 100 [ 80%]  (Sampling)
  Iteration: 90 / 100 [ 90%]  (Sampling)
  Iteration: 100 / 100 [100%]  (Sampling)
  
   Elapsed Time: 0.3 seconds (Warm-up)
                 0.27 seconds (Sampling)
                 0.57 seconds (Total)
  
  Initial log joint probability = -4956.4
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -6610.95
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -4709.91
  Exception: beta_lpdf: Second shape parameter[14] is 0, but must be > 0!  (in 'model_continuous' at line 192)
  
  
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -4551.1
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -5909.47
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Rejecting initial value:
    Error evaluating the log probability at the initial value.
  Exception: beta_lpdf: First shape parameter[5] is 0, but must be > 0!  (in 'model_continuous' at line 192)
  
  Initial log joint probability = -316.539
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -2387.7
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -3331.05
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -2279.49
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -2155.46
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -3306.94
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Rejecting initial value:
    Error evaluating the log probability at the initial value.
  Exception: beta_lpdf: First shape parameter[9] is 0, but must be > 0!  (in 'model_continuous' at line 192)
  
  Initial log joint probability = -188.536
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  Iteration: 1 / 1 [100%]  (Sampling)
  
   Elapsed Time: 0 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  Iteration: 1 / 1 [100%]  (Sampling)
  
   Elapsed Time: 0 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  Rejecting initial value:
    Error evaluating the log probability at the initial value.
  Exception: beta_lpdf: Second shape parameter[607] is 0, but must be > 0!  (in 'model_continuous' at line 192)
  
  Rejecting initial value:
    Error evaluating the log probability at the initial value.
  Exception: beta_lpdf: Second shape parameter[9] is 0, but must be > 0!  (in 'model_continuous' at line 192)
  
  Rejecting initial value:
    Error evaluating the log probability at the initial value.
  Exception: beta_lpdf: Second shape parameter[3] is 0, but must be > 0!  (in 'model_continuous' at line 192)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  Iteration: 1 / 1 [100%]  (Sampling)
  
   Elapsed Time: 0 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  Iteration: 1 / 1 [100%]  (Sampling)
  
   Elapsed Time: 0 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  Iteration: 1 / 1 [100%]  (Sampling)
  
   Elapsed Time: 0 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  Iteration: 1 / 1 [100%]  (Sampling)
  
   Elapsed Time: 0 seconds (Warm-up)
                 0 seconds (Sampling)
                 0 seconds (Total)
  
  Initial log joint probability = -228.234
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0.02 seconds (Sampling)
                 0.03 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 3
             adapt_window = 20
             term_buffer = 2
  
  
   Elapsed Time: 0.14 seconds (Warm-up)
                 0.36 seconds (Sampling)
                 0.5 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 3
             adapt_window = 20
             term_buffer = 2
  
  
   Elapsed Time: 0.23 seconds (Warm-up)
                 0.23 seconds (Sampling)
                 0.46 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 3
             adapt_window = 20
             term_buffer = 2
  
  
   Elapsed Time: 0.17 seconds (Warm-up)
                 0.25 seconds (Sampling)
                 0.42 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 3
             adapt_window = 20
             term_buffer = 2
  
  
   Elapsed Time: 0.13 seconds (Warm-up)
                 0.29 seconds (Sampling)
                 0.42 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0 seconds (Warm-up)
                 0 seconds (Sampling)
                 0 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 3
             adapt_window = 20
             term_buffer = 2
  
  
   Elapsed Time: 0.02 seconds (Warm-up)
                 0.03 seconds (Sampling)
                 0.05 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 3
             adapt_window = 20
             term_buffer = 2
  
  
   Elapsed Time: 0.02 seconds (Warm-up)
                 0.03 seconds (Sampling)
                 0.05 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 3
             adapt_window = 20
             term_buffer = 2
  
  
   Elapsed Time: 0.02 seconds (Warm-up)
                 0.03 seconds (Sampling)
                 0.05 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 3
             adapt_window = 20
             term_buffer = 2
  
  
   Elapsed Time: 0.02 seconds (Warm-up)
                 0.04 seconds (Sampling)
                 0.06 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  
   Elapsed Time: 0.07 seconds (Warm-up)
                 0.08 seconds (Sampling)
                 0.15 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  
   Elapsed Time: 0.08 seconds (Warm-up)
                 0.07 seconds (Sampling)
                 0.15 seconds (Total)
  
  In file included from /tmp/RtmpjTay9D/RLIBS_3beb7de81f56/BH/include/boost/config.hpp:39:0,
                   from /tmp/RtmpjTay9D/RLIBS_3beb7de81f56/BH/include/boost/fusion/support/config.hpp:11,
                   from /home/ripley/R/Lib32/rstan/include/boost_not_in_BH/boost/fusion/support/unused.hpp:10,
                   from file46488dd2d04.cpp:5:
  /tmp/RtmpjTay9D/RLIBS_3beb7de81f56/BH/include/boost/config/compiler/gcc.hpp:186:0: warning: "BOOST_NO_CXX11_RVALUE_REFERENCES" redefined
   #  define BOOST_NO_CXX11_RVALUE_REFERENCES
   ^
  <command-line>:0:0: note: this is the location of the previous definition
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0 seconds (Warm-up)
                 0.04 seconds (Sampling)
                 0.04 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.02 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0.02 seconds (Sampling)
                 0.03 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.02 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.02 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.03 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0.02 seconds (Sampling)
                 0.03 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.02 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.34 seconds (Warm-up)
                 0.28 seconds (Sampling)
                 0.62 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.18 seconds (Warm-up)
                 0.04 seconds (Sampling)
                 0.22 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.97 seconds (Warm-up)
                 0.6 seconds (Sampling)
                 1.57 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.13 seconds (Warm-up)
                 0.19 seconds (Sampling)
                 0.32 seconds (Total)
  
  Initial log joint probability = -3437.61
  Optimization terminated normally: 
    Convergence detected: relative change in objective function was below tolerance
  Initial log joint probability = -3105.9
  Optimization terminated normally: 
    Convergence detected: relative change in objective function was below tolerance
  Initial log joint probability = -581.92
  Optimization terminated normally: 
    Convergence detected: relative change in objective function was below tolerance
  Initial log joint probability = -233.123
  Optimization terminated normally: 
    Convergence detected: relative change in objective function was below tolerance
  Initial log joint probability = -196.099
  Optimization terminated normally: 
    Convergence detected: relative change in objective function was below tolerance
  
  SAMPLING FOR MODEL 'count' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  Iteration:  1 / 100 [  1%]  (Warmup)
  Iteration: 51 / 100 [ 51%]  (Sampling)
  Iteration: 100 / 100 [100%]  (Sampling)
  
   Elapsed Time: 0.08 seconds (Warm-up)
                 0.09 seconds (Sampling)
                 0.17 seconds (Total)
  
  
  SAMPLING FOR MODEL 'count' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  Iteration:  1 / 100 [  1%]  (Warmup)
  Iteration: 51 / 100 [ 51%]  (Sampling)
  Iteration: 100 / 100 [100%]  (Sampling)
  
   Elapsed Time: 0.1 seconds (Warm-up)
                 0.08 seconds (Sampling)
                 0.18 seconds (Total)
  
  
  SAMPLING FOR MODEL 'count' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  Iteration:  1 / 100 [  1%]  (Warmup)
  Iteration: 51 / 100 [ 51%]  (Sampling)
  Iteration: 100 / 100 [100%]  (Sampling)
  
   Elapsed Time: 0.06 seconds (Warm-up)
                 0.05 seconds (Sampling)
                 0.11 seconds (Total)
  
  
  SAMPLING FOR MODEL 'count' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  Iteration:  1 / 100 [  1%]  (Warmup)
  Iteration: 51 / 100 [ 51%]  (Sampling)
  Iteration: 100 / 100 [100%]  (Sampling)
  
   Elapsed Time: 0.06 seconds (Warm-up)
                 0.05 seconds (Sampling)
                 0.11 seconds (Total)
  
  
  SAMPLING FOR MODEL 'count' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  Iteration:  1 / 100 [  1%]  (Warmup)
  Iteration: 51 / 100 [ 51%]  (Sampling)
  Iteration: 100 / 100 [100%]  (Sampling)
  
   Elapsed Time: 0.06 seconds (Warm-up)
                 0.05 seconds (Sampling)
                 0.11 seconds (Total)
  
  
  SAMPLING FOR MODEL 'count' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
             adapt_window = 38
             term_buffer = 5
  
  Iteration:  1 / 100 [  1%]  (Warmup)
  Iteration: 51 / 100 [ 51%]  (Sampling)
  Iteration: 100 / 100 [100%]  (Sampling)
  
   Elapsed Time: 0.06 seconds (Warm-up)
                 0.05 seconds (Sampling)
                 0.11 seconds (Total)
  
  Initial log joint probability = -12673.6
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  Initial log joint probability = -173239
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0 seconds (Warm-up)
                 0 seconds (Sampling)
                 0 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0 seconds (Warm-up)
                 0 seconds (Sampling)
                 0 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.02 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  
   Elapsed Time: 0 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.01 seconds (Warm-up)
                 0.06 seconds (Sampling)
                 0.07 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 2
             adapt_window = 16
             term_buffer = 2
  
  
   Elapsed Time: 0.12 seconds (Warm-up)
                 0.04 seconds (Sampling)
                 0.16 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 6.06 seconds (Warm-up)
                 2.8 seconds (Sampling)
                 8.86 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 9.35 seconds (Warm-up)
                 2.96 seconds (Sampling)
                 12.31 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  Iteration: 1 / 10 [ 10%]  (Warmup)
  Iteration: 2 / 10 [ 20%]  (Warmup)
  Iteration: 3 / 10 [ 30%]  (Warmup)
  Iteration: 4 / 10 [ 40%]  (Warmup)
  Iteration: 5 / 10 [ 50%]  (Warmup)
  Iteration: 6 / 10 [ 60%]  (Sampling)
  Iteration: 7 / 10 [ 70%]  (Sampling)
  Iteration: 8 / 10 [ 80%]  (Sampling)
  Iteration: 9 / 10 [ 90%]  (Sampling)
  Iteration: 10 / 10 [100%]  (Sampling)
  
   Elapsed Time: 0 seconds (Warm-up)
                 0 seconds (Sampling)
                 0 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  Iteration: 1 / 10 [ 10%]  (Warmup)
  Iteration: 2 / 10 [ 20%]  (Warmup)
  Iteration: 3 / 10 [ 30%]  (Warmup)
  Iteration: 4 / 10 [ 40%]  (Warmup)
  Iteration: 5 / 10 [ 50%]  (Warmup)
  Iteration: 6 / 10 [ 60%]  (Sampling)
  Iteration: 7 / 10 [ 70%]  (Sampling)
  Iteration: 8 / 10 [ 80%]  (Sampling)
  Iteration: 9 / 10 [ 90%]  (Sampling)
  Iteration: 10 / 10 [100%]  (Sampling)
  
   Elapsed Time: 0 seconds (Warm-up)
                 0 seconds (Sampling)
                 0 seconds (Total)
  
  
  SAMPLING FOR MODEL 'binomial' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 1.06 seconds (Warm-up)
                 0.47 seconds (Sampling)
                 1.53 seconds (Total)
  
  
  SAMPLING FOR MODEL 'binomial' NOW (CHAIN 2).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 0.69 seconds (Warm-up)
                 0.39 seconds (Sampling)
                 1.08 seconds (Total)
  
  ------------------------------------------------------------
  EXPERIMENTAL ALGORITHM:
    This procedure has not been thoroughly tested and may be unstable
    or buggy. The interface is subject to change.
  ------------------------------------------------------------
  
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Begin eta adaptation.
  Iteration:   1 / 250 [  0%]  (Adaptation)
  Iteration:  50 / 250 [ 20%]  (Adaptation)
  Iteration: 100 / 250 [ 40%]  (Adaptation)
  Iteration: 150 / 250 [ 60%]  (Adaptation)
  Iteration: 200 / 250 [ 80%]  (Adaptation)
  Success! Found best value [eta = 1] earlier than expected.
  
  Begin stochastic gradient ascent.
    iter       ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
     100     -3e+03             1.000            1.000
     200     -3e+03             0.553            1.000
     300     -3e+03             0.055            0.106
     400     -3e+03             0.004            0.004   MEAN ELBO CONVERGED   MEDIAN ELBO CONVERGED
  Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
  This variational approximation is not guaranteed to be meaningful.
  
  Drawing a sample of size 1000 from the approximate posterior... 
  COMPLETED.
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 1.33 seconds (Warm-up)
                 0.87 seconds (Sampling)
                 2.2 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 2.39 seconds (Warm-up)
                 0.9 seconds (Sampling)
                 3.29 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  Iteration: 1 / 10 [ 10%]  (Warmup)
  Iteration: 2 / 10 [ 20%]  (Warmup)
  Iteration: 3 / 10 [ 30%]  (Warmup)
  Iteration: 4 / 10 [ 40%]  (Warmup)
  Iteration: 5 / 10 [ 50%]  (Warmup)
  Iteration: 6 / 10 [ 60%]  (Sampling)
  Iteration: 7 / 10 [ 70%]  (Sampling)
  Iteration: 8 / 10 [ 80%]  (Sampling)
  Iteration: 9 / 10 [ 90%]  (Sampling)
  Iteration: 10 / 10 [100%]  (Sampling)
  
   Elapsed Time: 0 seconds (Warm-up)
                 0 seconds (Sampling)
                 0 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  Iteration: 1 / 10 [ 10%]  (Warmup)
  Iteration: 2 / 10 [ 20%]  (Warmup)
  Iteration: 3 / 10 [ 30%]  (Warmup)
  Iteration: 4 / 10 [ 40%]  (Warmup)
  Iteration: 5 / 10 [ 50%]  (Warmup)
  Iteration: 6 / 10 [ 60%]  (Sampling)
  Iteration: 7 / 10 [ 70%]  (Sampling)
  Iteration: 8 / 10 [ 80%]  (Sampling)
  Iteration: 9 / 10 [ 90%]  (Sampling)
  Iteration: 10 / 10 [100%]  (Sampling)
  
   Elapsed Time: 0 seconds (Warm-up)
                 0.01 seconds (Sampling)
                 0.01 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  Iteration: 1 / 10 [ 10%]  (Warmup)
  Iteration: 2 / 10 [ 20%]  (Warmup)
  Iteration: 3 / 10 [ 30%]  (Warmup)
  Iteration: 4 / 10 [ 40%]  (Warmup)
  Iteration: 5 / 10 [ 50%]  (Warmup)
  Iteration: 6 / 10 [ 60%]  (Sampling)
  Iteration: 7 / 10 [ 70%]  (Sampling)
  Iteration: 8 / 10 [ 80%]  (Sampling)
  Iteration: 9 / 10 [ 90%]  (Sampling)
  Iteration: 10 / 10 [100%]  (Sampling)
  
   Elapsed Time: 0 seconds (Warm-up)
                 0 seconds (Sampling)
                 0 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  Iteration: 1 / 10 [ 10%]  (Warmup)
  Iteration: 2 / 10 [ 20%]  (Warmup)
  Iteration: 3 / 10 [ 30%]  (Warmup)
  Iteration: 4 / 10 [ 40%]  (Warmup)
  Iteration: 5 / 10 [ 50%]  (Warmup)
  Iteration: 6 / 10 [ 60%]  (Sampling)
  Iteration: 7 / 10 [ 70%]  (Sampling)
  Iteration: 8 / 10 [ 80%]  (Sampling)
  Iteration: 9 / 10 [ 90%]  (Sampling)
  Iteration: 10 / 10 [100%]  (Sampling)
  
   Elapsed Time: 0 seconds (Warm-up)
                 0 seconds (Sampling)
                 0 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 3.35 seconds (Warm-up)
                 1.29 seconds (Sampling)
                 4.64 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 2.49 seconds (Warm-up)
                 1.32 seconds (Sampling)
                 3.81 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 3.38 seconds (Warm-up)
                 1.32 seconds (Sampling)
                 4.7 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 2.49 seconds (Warm-up)
                 1.32 seconds (Sampling)
                 3.81 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 0.71 seconds (Warm-up)
                 0.35 seconds (Sampling)
                 1.06 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 0.92 seconds (Warm-up)
                 0.36 seconds (Sampling)
                 1.28 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 0.69 seconds (Warm-up)
                 0.35 seconds (Sampling)
                 1.04 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 0.89 seconds (Warm-up)
                 0.35 seconds (Sampling)
                 1.24 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 2.21 seconds (Warm-up)
                 0.87 seconds (Sampling)
                 3.08 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 1.66 seconds (Warm-up)
                 1.28 seconds (Sampling)
                 2.94 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 0.7 seconds (Warm-up)
                 0.33 seconds (Sampling)
                 1.03 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 0.89 seconds (Warm-up)
                 0.43 seconds (Sampling)
                 1.32 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 0.88 seconds (Warm-up)
                 0.33 seconds (Sampling)
                 1.21 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 1.11 seconds (Warm-up)
                 0.33 seconds (Sampling)
                 1.44 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 2.18 seconds (Warm-up)
                 1.32 seconds (Sampling)
                 3.5 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 2.11 seconds (Warm-up)
                 1.19 seconds (Sampling)
                 3.3 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 4.8 seconds (Warm-up)
                 3.76 seconds (Sampling)
                 8.56 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  Iteration:   1 / 400 [  0%]  (Warmup)
  Iteration: 201 / 400 [ 50%]  (Sampling)
  Iteration: 400 / 400 [100%]  (Sampling)
  
   Elapsed Time: 4.46 seconds (Warm-up)
                 2.24 seconds (Sampling)
                 6.7 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  
   Elapsed Time: 2.55 seconds (Warm-up)
                 2.39 seconds (Sampling)
                 4.94 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  
   Elapsed Time: 4.11 seconds (Warm-up)
                 1.9 seconds (Sampling)
                 6.01 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  
   Elapsed Time: 13.56 seconds (Warm-up)
                 17.16 seconds (Sampling)
                 30.72 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  
   Elapsed Time: 12.65 seconds (Warm-up)
                 11.84 seconds (Sampling)
                 24.49 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  
   Elapsed Time: 0.31 seconds (Warm-up)
                 0.27 seconds (Sampling)
                 0.58 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  
   Elapsed Time: 0.39 seconds (Warm-up)
                 0.31 seconds (Sampling)
                 0.7 seconds (Total)
  
  
  SAMPLING FOR MODEL 'lm' NOW (CHAIN 1).
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: No variance estimation is
           performed for num_warmup < 20
  
  Iteration: 1 / 10 [ 10%]  (Warmup)
  Iteration: 2 / 10 [ 20%]  (Warmup)
  Iteration: 3 / 10 [ 30%]  (Warmup)
  Iteration: 4 / 10 [ 40%]  (Warmup)
  Iteration: 5 / 10 [ 50%]  (Warmup)
  Iteration: 6 / 10 [ 60%]  (Sampling)
  Iteration: 7 / 10 [ 70%]  (Sampling)
  Iteration: 8 / 10 [ 80%]  (Sampling)
  Iteration: 9 / 10 [ 90%]  (Sampling)
  Iteration: 10 / 10 [100%]  (Sampling)
  
   Elapsed Time: 0 seconds (Warm-up)
                 0 seconds (Sampling)
                 0 seconds (Total)
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  
  Gradient evaluation took 0 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
  Adjust your expectations accordingly!
  
  
  
   Elapsed Time: 4.54 seconds (Warm-up)
                 3.62 seconds (Sampling)
                 8.16 seconds (Total)
  
  
   Elapsed Time: 4.43 seconds (Warm-up)
                 3.23 seconds (Sampling)
                 7.66 seconds (Total)
  
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 1858 SKIPPED: 2 FAILED: 1
  1. Error: loo issues errors/warnings (@test_loo.R#90) 
  
  Error: testthat unit tests failed
  Execution halted