• using R Under development (unstable) (2026-05-28 r90087)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc (GCC) 15.2.1 20260123 (Red Hat 15.2.1-7)     GNU Fortran (GCC) 15.2.1 20260123 (Red Hat 15.2.1-7)
• running under: Fedora Linux 42 (Workstation Edition)
• using session charset: UTF-8 * current time: 2026-05-30 22:57:49 UTC
• using option ‘--no-stop-on-test-error’
• checking for file ‘accelEE/DESCRIPTION’ ... OK
• checking extension type ... Package
• this is package ‘accelEE’ version ‘0.3.1’
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• checking whether package ‘accelEE’ can be installed ... [8s/10s] OK See the install log for details.
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• checking for unstated dependencies in examples ... OK
• checking examples ... [5s/12s] ERROR Running examples in ‘accelEE-Ex.R’ failed The error most likely occurred in: > ### Name: accelEE-function > ### Title: Predict energy expenditure for accelerometry data > ### Aliases: accelEE-function accelEE wrap_2RM crouter15 hildebrand_linear > ### hildebrand_nonlinear montoye sojourn staudenmayer > > ### ** Examples > > > #### Below, note the variations throughout the examples, > #### showing different ways you can customize the output > > > > ## Raw acceleration examples: > > if (isTRUE(requireNamespace("read.gt3x", quietly = TRUE))) { + + f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x") + d <- stats::setNames( + read.gt3x::read.gt3x(f, asDataFrame = TRUE, imputeZeroes = TRUE), + c("Timestamp", "Accelerometer_X", "Accelerometer_Y", "Accelerometer_Z") + )[1:30000, ] + + utils::head( + accelEE( + d, "Hibbing 2018", algorithm = 1, + site = c("Left Wrist", "Right Wrist"), + warn_high_low = FALSE, shrink_output = FALSE + ) + ) + + utils::head( + accelEE( + d, c("Hildebrand Linear", "Hildebrand Non-Linear"), age = "adult", + monitor = "ActiGraph", location = "Wrist", warn_high_low = FALSE, + ee_vars = c("METs", "kcal"), output_epoch = "60 sec" + ) + ) + + accelEE( + d, c( + "Montoye 2017", "Staudenmayer Linear", + "Staudenmayer Random Forest" + ), side = "left", ee_vars = "VO2", combine = FALSE + ) + + }  *** caught segfault *** address 0x1, cause 'memory not mapped' Traceback:  1: predict.nnet(model, newdata = d)  2: stats::predict(model, newdata = d)  3: check_values(stats::predict(model, newdata = d), min_mets, max_mets, label, "MET", "MET(s)", warn_high_low)  4: .Call(dplyr_mask_eval_all_mutate, quo, private)  5: eval()  6: mask$eval_all_mutate(quo)  7: mutate_col(dots[[i]], data, mask, new_columns)  8: withCallingHandlers(for (i in seq_along(dots)) { poke_error_context(dots, i, mask = mask) context_poke("column", old_current_column) new_columns <- mutate_col(dots[[i]], data, mask, new_columns)}, error = dplyr_error_handler(dots = dots, mask = mask, bullets = mutate_bullets, error_call = error_call, error_class = "dplyr:::mutate_error"), warning = dplyr_warning_handler(state = warnings_state, mask = mask, error_call = error_call))  9: mutate_cols(.data, dplyr_quosures(...), by) 10: mutate.data.frame(., `:=`(!!as.name(out_name), check_values(stats::predict(model, newdata = d), min_mets, max_mets, label, "MET", "MET(s)", warn_high_low))) 11: dplyr::mutate(., `:=`(!!as.name(out_name), check_values(stats::predict(model, newdata = d), min_mets, max_mets, label, "MET", "MET(s)", warn_high_low))) 12: d %>% dplyr::mutate(`:=`(!!as.name(out_name), check_values(stats::predict(model, newdata = d), min_mets, max_mets, label, "MET", "MET(s)", warn_high_low))) 13: predict_model(d, out_name, model, label, min_mets, max_mets, warn_high_low) 14: predict_montoye(., "METs_left_wrist", "left", side, EE.Data::montoye_lw, "Montoye Left Wrist", min_mets, max_mets, warn_high_low) 15: predict_montoye(., "METs_right_wrist", "right", side, EE.Data::montoye_rw, "Montoye Right Wrist", min_mets, max_mets, warn_high_low) 16: dplyr::rename_with(., function(x, met_name, tag) { gsub(met_name, "", x) %>% paste0(tag, "_METs_", .)}, dplyr::matches(met_name), met_name = met_name, tag = tag) 17: is.factor(x) 18: gsub("[_.-]+", "_", names(.)) 19: stats::setNames(., gsub("[_.-]+", "_", names(.))) 20: dplyr::mutate(., dplyr::across(dplyr::contains("METs"), ~check_values(.x, minimum = min_mets, maximum = max_mets, label = gsub("[\\._\\-]+", "", toupper(tag)), variable = "MET", units = "MET(s)", warn_high_low = warn_high_low)), dplyr::across(dplyr::contains("METs"), ~.x * met_mlkgmin, .names = "{gsub(\"METs\", \"vo2_mlkgmin\", .col)}"), dplyr::across(dplyr::contains("vo2_mlkgmin"), ~.x/1000 * PAutilities::get_kcal_vo2_conversion(RER, "Lusk"), .names = "{gsub(\"vo2_mlkgmin\", \"kcal_kgmin\", .col)}")) 21: is.factor(x) 22: gsub("_+$", "", names(.)) 23: stats::setNames(., gsub("_+$", "", names(.))) 24: d %>% dplyr::rename_with(function(x, met_name, tag) { gsub(met_name, "", x) %>% paste0(tag, "_METs_", .)}, dplyr::matches(met_name), met_name = met_name, tag = tag) %>% stats::setNames(., gsub("[_.-]+", "_", names(.))) %>% dplyr::mutate(dplyr::across(dplyr::contains("METs"), ~check_values(.x, minimum = min_mets, maximum = max_mets, label = gsub("[\\._\\-]+", "", toupper(tag)), variable = "MET", units = "MET(s)", warn_high_low = warn_high_low)), dplyr::across(dplyr::contains("METs"), ~.x * met_mlkgmin, .names = "{gsub(\"METs\", \"vo2_mlkgmin\", .col)}"), dplyr::across(dplyr::contains("vo2_mlkgmin"), ~.x/1000 * PAutilities::get_kcal_vo2_conversion(RER, "Lusk"), .names = "{gsub(\"vo2_mlkgmin\", \"kcal_kgmin\", .col)}")) %>% stats::setNames(., gsub("_+$", "", names(.))) 25: met_expand(., "METs", "montoye", met_mlkgmin, min_mets, max_mets, RER, warn_high_low) 26: d %>% predict_montoye("METs_left_wrist", "left", side, EE.Data::montoye_lw, "Montoye Left Wrist", min_mets, max_mets, warn_high_low) %>% predict_montoye("METs_right_wrist", "right", side, EE.Data::montoye_rw, "Montoye Right Wrist", min_mets, max_mets, warn_high_low) %>% met_expand("METs", "montoye", met_mlkgmin, min_mets, max_mets, RER, warn_high_low) 27: montoye(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ...) 28: lapply(X = X, FUN = FUN, ...) 29: sapply(., switch, `Crouter 2006` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Crouter 2006", ..., met_name = "METs", tag = "crouter06"), `Crouter 2010` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Crouter 2010", ..., met_name = "METs", tag = "crouter10"), `Crouter 2012` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Crouter 2012", ..., met_name = "METs", tag = "crouter12"), `Crouter 2015` = crouter15(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, ...), `Hibbing 2018` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Hibbing 2018", ..., met_name = "METs", tag = "hibbing18"), `Hildebrand Linear` = hildebrand_linear(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ...), `Hildebrand Non-Linear` = hildebrand_nonlinear(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ...), `Montoye 2017` = montoye(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ...), SIP = sojourn(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, method = "SIP", ..., met_name = "METs", tag = "SIP"), `Sojourn 1x` = sojourn(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, method = "Sojourn 1x", ..., met_name = "METs", tag = "soj_1x"), `Sojourn 3x` = sojourn(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, method = "Sojourn 3x", ..., met_name = "METs", tag = "soj_3x"), `Staudenmayer Linear` = staudenmayer(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ..., select = "METs_lm"), `Staudenmayer Random Forest` = staudenmayer(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ..., select = "METs_rf"), `Staudenmayer Both` = staudenmayer(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ..., select = c("METs_lm", "METs_rf")), stop("Invalid value passed for `method` argument:", " see ?args(accelEE::accelEE) for options", call. = FALSE), simplify = FALSE) 30: is.factor(x) 31: gsub("^Staudenmayer Both$", "Staudenmayer", names(.)) 32: stats::setNames(., gsub("^Staudenmayer Both$", "Staudenmayer", names(.))) 33: method %>% sapply(switch, `Crouter 2006` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Crouter 2006", ..., met_name = "METs", tag = "crouter06"), `Crouter 2010` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Crouter 2010", ..., met_name = "METs", tag = "crouter10"), `Crouter 2012` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Crouter 2012", ..., met_name = "METs", tag = "crouter12"), `Crouter 2015` = crouter15(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, ...), `Hibbing 2018` = wrap_2RM(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, method = "Hibbing 2018", ..., met_name = "METs", tag = "hibbing18"), `Hildebrand Linear` = hildebrand_linear(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ...), `Hildebrand Non-Linear` = hildebrand_nonlinear(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ...), `Montoye 2017` = montoye(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ...), SIP = sojourn(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, method = "SIP", ..., met_name = "METs", tag = "SIP"), `Sojourn 1x` = sojourn(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, method = "Sojourn 1x", ..., met_name = "METs", tag = "soj_1x"), `Sojourn 3x` = sojourn(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, shrink_output = shrink_output, verbose = verbose, method = "Sojourn 3x", ..., met_name = "METs", tag = "soj_3x"), `Staudenmayer Linear` = staudenmayer(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ..., select = "METs_lm"), `Staudenmayer Random Forest` = staudenmayer(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ..., select = "METs_rf"), `Staudenmayer Both` = staudenmayer(d, time_var, output_epoch, warn_high_low = warn_high_low, met_mlkgmin = met_mlkgmin, RER = RER, feature_calc = feature_calc, shrink_output = shrink_output, verbose = verbose, ..., select = c("METs_lm", "METs_rf")), stop("Invalid value passed for `method` argument:", " see ?args(accelEE::accelEE) for options", call. = FALSE), simplify = FALSE) %>% stats::setNames(., gsub("^Staudenmayer Both$", "Staudenmayer", names(.))) 34: accelEE(d, c("Montoye 2017", "Staudenmayer Linear", "Staudenmayer Random Forest"), side = "left", ee_vars = "VO2", combine = FALSE) An irrecoverable exception occurred. R is aborting now ...
• checking PDF version of manual ... [9s/13s] OK
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• checking for non-standard things in the check directory ... OK
• checking for detritus in the temp directory ... OK
• DONE

Status: 1 ERROR