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 ...