Last updated on 2026-09-28 01:50:44 CEST.
| Package | OK | ERROR |
|---|---|---|
| adaptDiag | 13 | |
| bayesDP | 13 | |
| goldilocks | 12 | 1 |
| joineR | 13 | |
| joineRML | 13 |
Current CRAN status: OK: 13
Current CRAN status: OK: 13
Current CRAN status: OK: 12, ERROR: 1
Version: 1.0.0
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
--- re-building ‘advent.Rmd’ using rmarkdown
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$future_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)
2: fill_predictive_imputations(imputations = future, rows = future_treatment, values = impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$future_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation))
3: impute_predictive_draws(data_in = data_interim, hazards = post_lambda, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, binary_imputation = predictive_binary_imputation, check_futility = check_futility)
4: evaluate_interim_decision(data_interim = data_interim, look = i, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, active_followup = active_followup_at(data_total, look_time), end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, method = method, binary_imputation = binary_imputation, check_futility = check_futility, Qn = Qn[i])
5: survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)
6: doTryCatch(return(expr), name, parentenv, handler)
7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
8: tryCatchList(expr, classes, parentenv, handlers)
9: tryCatch({ if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result)}, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error))})
10: FUN(X[[i]], ...)
11: lapply(X = S, FUN = FUN, ...)
12: doTryCatch(return(expr), name, parentenv, handler)
13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: tryCatchList(expr, classes, parentenv, handlers)
15: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
16: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
17: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
18: FUN(X[[i]], ...)
19: lapply(seq_len(cores), inner.do)
20: mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive)
21: pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)
22: (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, treatment = 1), prop_loss = 0, alternative = "greater", h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", imputed_final = FALSE, empty_interval = c("prior", "propagate", "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, Qn = 1, rmst_tau = end_of_study) { Call <- match.call() Arguments <- capture_arguments(sim_trials, environment()) method <- normalize_analysis_method(method) Arguments$method <- method empty_interval <- match.arg(empty_interval) binary_imputation <- match.arg(binary_imputation) backend <- match.arg(backend) Arguments$empty_interval <- empty_interval Arguments$binary_imputation <- binary_imputation Arguments$backend <- backend requested_backend <- backend caller_rng_kind <- RNGkind() validate_positive_integer_scalar(N_trials, "N_trials") single_arm <- is.null(hazard_control) if (!single_arm) { rand_ratio <- validate_randomization_args(N_total, block, rand_ratio, allocation_name = "rand_ratio") Arguments$rand_ratio <- rand_ratio } prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) Arguments$prop_loss <- prop_loss validate_logical_scalar(return_trace, "return_trace") validate_positive_integer_scalar(N_impute, "N_impute") validate_positive_integer_scalar(N_mcmc, "N_mcmc") validate_single_probability(mc_conf_level, "mc_conf_level", upper_open = TRUE) if (mc_conf_level <= 0.5) { stop("'mc_conf_level' must be greater than 0.5 and less than 1") } validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > 0), N_impute = N_impute) if (identical(method, "rmst")) { validate_endpoint_time(end_of_study, cutpoints, "end_of_study") validate_rmst_args(rmst_tau, end_of_study, h0) validate_analysis_configuration(method, alternative, is.null(hazard_control), imputed_final) } validate_positive_integer_scalar(ncores, "ncores") execution <- resolve_sim_execution(backend = backend, ncores = ncores, N_trials = N_trials) backend <- execution$backend workers <- execution$workers stream_seed <- NULL if (!is.null(seed)) { if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || !is.finite(seed) || seed != floor(seed)) { stop("'seed' must be NULL or a single integer value") } old_kind <- RNGkind() old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE) if (old_seed_exists) { old_seed <- get(".Random.seed", envir = .GlobalEnv, inherits = FALSE) } on.exit({ do.call(RNGkind, as.list(old_kind)) if (old_seed_exists) { assign(".Random.seed", old_seed, envir = .GlobalEnv) } else if (exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) { rm(".Random.seed", envir = .GlobalEnv) } }, add = TRUE) stream_seed <- seed trial_streams <- make_rng_streams(stream_seed, N_trials) } else if (backend == "psock") { stream_seed <- sample.int(.Machine$integer.max - 1L, size = 1L) trial_streams <- make_rng_streams(stream_seed, N_trials) } else { trial_streams <- NULL } rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { caller_rng_kind[1] } else { "L'Ecuyer-CMRG" }, seed_policy = if (!is.null(seed)) { "explicit_preserve_caller" } else if (backend == "psock") { "caller_derived_psock" } else { "caller_state" }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), stream_seed = stream_seed) survival_adapt_fn <- if (backend == "psock") { make_psock_callable("survival_adapt") } else { survival_adapt } survival_adapt_wrapper <- function(x) { tryCatch({ if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result) }, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error)) }) } trial_index <- seq_len(N_trials) trial_results <- switch(backend, sequential = lapply(trial_index, survival_adapt_wrapper), fork = pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers), psock = { active_cluster <- make_sim_cluster(workers) on.exit(stop_sim_cluster(active_cluster), add = TRUE) initialize_sim_cluster(active_cluster) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) }) failed <- vapply(trial_results, function(x) is.null(x$result), logical(1)) failed_results <- trial_results[failed] failures <- data.frame(trial = vapply(failed_results, `[[`, integer(1), "trial"), error_class = vapply(failed_results, `[[`, character(1), "error_class"), message = vapply(failed_results, `[[`, character(1), "message"), stringsAsFactors = FALSE) if (all(failed)) { error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", failures$message[1L])) error$failures <- failures class(error) <- c("goldilocks_all_trials_failed", class(error)) stop(error) } successful_trials <- trial_results[!failed] successful_results <- lapply(successful_trials, `[[`, "result") if (return_trace) { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) traces <- bind_rows(lapply(seq_along(successful_results), function(i) { trace <- successful_results[[i]]$trace trial <- successful_trials[[i]]$trial trace$trial <- rep.int(trial, nrow(trace)) trace[c("trial", setdiff(names(trace), "trial"))] })) out <- list(sims = sims, traces = traces, failures = failures, call = Call) } else { sims <- bind_rows(successful_results) out <- list(sims = sims, failures = failures, call = Call) } attr(out$sims, "arguments") <- NULL attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study) attr(out, "decision_design") <- attr(successful_results[[1]], "decision_design", exact = TRUE) attr(out, "prior_design") <- attr(successful_results[[1]], "prior_design", exact = TRUE) attr(out, "rng_metadata") <- rng_metadata attr(out, "arguments") <- Arguments attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, backend = backend, selection_reason = execution$reason, requested_ncores = as.integer(ncores), workers = as.integer(workers), tasks = as.integer(N_trials)) if (nrow(failures) > 0L) { warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", "for details.", call. = FALSE) } return(out)})(N_total = 750, lambda = c(0.0666666666666667, 0.166666666666667, 0.333333333333333, 0.5, 0.666666666666667, 0.833333333333333, 1, 1.1), lambda_time = c(30L, 60L, 90L, 120L, 150L, 180L, 210L), interim_look = c(350, 450, 550, 650), end_of_study = 360L, prior_surv = c(0.5, 0.001, 0.5, 0.001, 0.5, 0.001, 0.5, 0.001, 5, 10000), prior_surv_final = c(shape = 0.5, rate = 0.001 ), prior_bin = c(0.5, 0.5), bin_method = "quadrature", block = 2, rand_ratio = c(control = 1, treatment = 1), alternative = "less", Fn = c(0.05, 0.1, 0.1, 0.1), Sn = c(0.95, 0.9, 0.85, 0.8), N_impute = 30, empty_interval = "prior", method = "bayes-bin", imputed_final = TRUE, ncores = 2, cutpoints = c(90, 104, 150, 210), prop_loss = 0.075, N_trials = 500, hazard_treatment = c(0.00011167, 0.002197976, 0.003163208, 0.002839089, 0.000494053), hazard_control = c(0.00011167, 0.002197976, 0.003163208, 0.002839089, 0.000494053), h0 = 0.15, prob_ha = 0.956, return_trace = TRUE, seed = 4610)
23: do.call(sim_trials, c(advent_effectiveness_args, list(N_trials = 500, hazard_treatment = eff_hazard_per_day, hazard_control = eff_hazard_per_day, h0 = 0.15, prob_ha = 0.956, return_trace = TRUE, seed = 4610)))
24: eval(expr, envir)
25: eval(expr, envir)
26: withVisible(eval(expr, envir))
27: withCallingHandlers(code, error = function (e) rlang::entrace(e), message = function (cnd) { watcher$capture_plot_and_output() if (on_message$capture) { watcher$push(cnd) } if (on_message$silence) { invokeRestart("muffleMessage") }}, warning = function (cnd) { if (getOption("warn") >= 2 || getOption("warn") < 0) { return() } watcher$capture_plot_and_output() if (on_warning$capture) { cnd <- sanitize_call(cnd) watcher$push(cnd) } if (on_warning$silence) { invokeRestart("muffleWarning") }}, error = function (cnd) { watcher$capture_plot_and_output() cnd <- sanitize_call(cnd) watcher$push(cnd) switch(on_error, continue = invokeRestart("eval_continue"), stop = invokeRestart("eval_stop"), error = NULL)})
28: eval(call)
29: eval(call)
30: with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers)
31: doWithOneRestart(return(expr), restart)
32: withOneRestart(expr, restarts[[1L]])
33: withRestartList(expr, restarts[-nr])
34: doWithOneRestart(return(expr), restart)
35: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
36: withRestartList(expr, restarts)
37: withRestarts(with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
38: evaluate::evaluate(...)
39: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options))
40: in_dir(input_dir(), expr)
41: in_input_dir(evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options)))
42: eng_r(options)
43: block_exec(params)
44: call_block(x)
45: process_group(group)
46: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) })
47: with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global()))
48: xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global())), function(loc) { setwd(wd) write_utf8(res, output %n% stdout()) paste0("\nQuitting from ", loc, if (!is.null(error)) paste0("\n", rule(), error, "\n", rule()))}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
49: process_file(text, output)
50: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
51: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output_dir = getwd(), ...)
52: vweave_rmarkdown(...)
53: engine$weave(file, quiet = quiet, encoding = enc)
54: doTryCatch(return(expr), name, parentenv, handler)
55: tryCatchOne(expr, names, parentenv, handlers[[1L]])
56: tryCatchList(expr, classes, parentenv, handlers)
57: tryCatch({ engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/Rtmp79XvSm/file86582cb28e6e.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$future_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)
2: fill_predictive_imputations(imputations = future, rows = future_treatment, values = impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$future_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation))
3: impute_predictive_draws(data_in = data_interim, hazards = post_lambda, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, binary_imputation = predictive_binary_imputation, check_futility = check_futility)
4: evaluate_interim_decision(data_interim = data_interim, look = i, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, active_followup = active_followup_at(data_total, look_time), end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, method = method, binary_imputation = binary_imputation, check_futility = check_futility, Qn = Qn[i])
5: survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)
6: doTryCatch(return(expr), name, parentenv, handler)
7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
8: tryCatchList(expr, classes, parentenv, handlers)
9: tryCatch({ if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result)}, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error))})
10: FUN(X[[i]], ...)
11: lapply(X = S, FUN = FUN, ...)
12: doTryCatch(return(expr), name, parentenv, handler)
13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: tryCatchList(expr, classes, parentenv, handlers)
15: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
16: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
17: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
18: FUN(X[[i]], ...)
19: lapply(seq_len(cores), inner.do)
20: mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive)
21: pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)
22: (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, treatment = 1), prop_loss = 0, alternative = "greater", h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", imputed_final = FALSE, empty_interval = c("prior", "propagate", "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, Qn = 1, rmst_tau = end_of_study) { Call <- match.call() Arguments <- capture_arguments(sim_trials, environment()) method <- normalize_analysis_method(method) Arguments$method <- method empty_interval <- match.arg(empty_interval) binary_imputation <- match.arg(binary_imputation) backend <- match.arg(backend) Arguments$empty_interval <- empty_interval Arguments$binary_imputation <- binary_imputation Arguments$backend <- backend requested_backend <- backend caller_rng_kind <- RNGkind() validate_positive_integer_scalar(N_trials, "N_trials") single_arm <- is.null(hazard_control) if (!single_arm) { rand_ratio <- validate_randomization_args(N_total, block, rand_ratio, allocation_name = "rand_ratio") Arguments$rand_ratio <- rand_ratio } prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) Arguments$prop_loss <- prop_loss validate_logical_scalar(return_trace, "return_trace") validate_positive_integer_scalar(N_impute, "N_impute") validate_positive_integer_scalar(N_mcmc, "N_mcmc") validate_single_probability(mc_conf_level, "mc_conf_level", upper_open = TRUE) if (mc_conf_level <= 0.5) { stop("'mc_conf_level' must be greater than 0.5 and less than 1") } validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > 0), N_impute = N_impute) if (identical(method, "rmst")) { validate_endpoint_time(end_of_study, cutpoints, "end_of_study") validate_rmst_args(rmst_tau, end_of_study, h0) validate_analysis_configuration(method, alternative, is.null(hazard_control), imputed_final) } validate_positive_integer_scalar(ncores, "ncores") execution <- resolve_sim_execution(backend = backend, ncores = ncores, N_trials = N_trials) backend <- execution$backend workers <- execution$workers stream_seed <- NULL if (!is.null(seed)) { if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || !is.finite(seed) || seed != floor(seed)) { stop("'seed' must be NULL or a single integer value") } old_kind <- RNGkind() old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE) if (old_seed_exists) { old_seed <- get(".Random.seed", envir = .GlobalEnv, inherits = FALSE) } on.exit({ do.call(RNGkind, as.list(old_kind)) if (old_seed_exists) { assign(".Random.seed", old_seed, envir = .GlobalEnv) } else if (exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) { rm(".Random.seed", envir = .GlobalEnv) } }, add = TRUE) stream_seed <- seed trial_streams <- make_rng_streams(stream_seed, N_trials) } else if (backend == "psock") { stream_seed <- sample.int(.Machine$integer.max - 1L, size = 1L) trial_streams <- make_rng_streams(stream_seed, N_trials) } else { trial_streams <- NULL } rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { caller_rng_kind[1] } else { "L'Ecuyer-CMRG" }, seed_policy = if (!is.null(seed)) { "explicit_preserve_caller" } else if (backend == "psock") { "caller_derived_psock" } else { "caller_state" }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), stream_seed = stream_seed) survival_adapt_fn <- if (backend == "psock") { make_psock_callable("survival_adapt") } else { survival_adapt } survival_adapt_wrapper <- function(x) { tryCatch({ if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL } list(trial = as.integer(x), result = result) }, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error)) }) } trial_index <- seq_len(N_trials) trial_results <- switch(backend, sequential = lapply(trial_index, survival_adapt_wrapper), fork = pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers), psock = { active_cluster <- make_sim_cluster(workers) on.exit(stop_sim_cluster(active_cluster), add = TRUE) initialize_sim_cluster(active_cluster) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) }) failed <- vapply(trial_results, function(x) is.null(x$result), logical(1)) failed_results <- trial_results[failed] failures <- data.frame(trial = vapply(failed_results, `[[`, integer(1), "trial"), error_class = vapply(failed_results, `[[`, character(1), "error_class"), message = vapply(failed_results, `[[`, character(1), "message"), stringsAsFactors = FALSE) if (all(failed)) { error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", failures$message[1L])) error$failures <- failures class(error) <- c("goldilocks_all_trials_failed", class(error)) stop(error) } successful_trials <- trial_results[!failed] successful_results <- lapply(successful_trials, `[[`, "result") if (return_trace) { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) traces <- bind_rows(lapply(seq_along(successful_results), function(i) { trace <- successful_results[[i]]$trace trial <- successful_trials[[i]]$trial trace$trial <- rep.int(trial, nrow(trace)) trace[c("trial", setdiff(names(trace), "trial"))] })) out <- list(sims = sims, traces = traces, failures = failures, call = Call) } else { sims <- bind_rows(successful_results) out <- list(sims = sims, failures = failures, call = Call) } attr(out$sims, "arguments") <- NULL attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study) attr(out, "decision_design") <- attr(successful_results[[1]], "decision_design", exact = TRUE) attr(out, "prior_design") <- attr(successful_results[[1]], "prior_design", exact = TRUE) attr(out, "rng_metadata") <- rng_metadata attr(out, "arguments") <- Arguments attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, backend = backend, selection_reason = execution$reason, requested_ncores = as.integer(ncores), workers = as.integer(workers), tasks = as.integer(N_trials)) if (nrow(failures) > 0L) { warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", "for details.", call. = FALSE) } return(out)})(N_total = 750, lambda = c(0.0666666666666667, 0.166666666666667, 0.333333333333333, 0.5, 0.666666666666667, 0.833333333333333, 1, 1.1), lambda_time = c(30L, 60L, 90L, 120L, 150L, 180L, 210L), interim_look = c(350, 450, 550, 650), end_of_study = 360L, prior_surv = c(0.5, 0.001, 0.5, 0.001, 0.5, 0.001, 0.5, 0.001, 5, 10000), prior_surv_final = c(shape = 0.5, rate = 0.001 ), prior_bin = c(0.5, 0.5), bin_method = "quadrature", block = 2, rand_ratio = c(control = 1, treatment = 1), alternative = "less", Fn = c(0.05, 0.1, 0.1, 0.1), Sn = c(0.95, 0.9, 0.85, 0.8), N_impute = 30, empty_interval = "prior", method = "bayes-bin", imputed_final = TRUE, ncores = 2, cutpoints = c(90, 104, 150, 210), prop_loss = 0.075, N_trials = 500, hazard_treatment = c(0.00011167, 0.002197976, 0.003163208, 0.002839089, 0.000494053), hazard_control = c(0.00011167, 0.002197976, 0.003163208, 0.002839089, 0.000494053), h0 = 0.15, prob_ha = 0.956, return_trace = TRUE, seed = 4610)
23: do.call(sim_trials, c(advent_effectiveness_args, list(N_trials = 500, hazard_treatment = eff_hazard_per_day, hazard_control = eff_hazard_per_day, h0 = 0.15, prob_ha = 0.956, return_trace = TRUE, seed = 4610)))
24: eval(expr, envir)
25: eval(expr, envir)
26: withVisible(eval(expr, envir))
27: withCallingHandlers(code, error = function (e) rlang::entrace(e), message = function (cnd) { watcher$capture_plot_and_output() if (on_message$capture) { watcher$push(cnd) } if (on_message$silence) { invokeRestart("muffleMessage") }}, warning = function (cnd) { if (getOption("warn") >= 2 || getOption("warn") < 0) { return() } watcher$capture_plot_and_output() if (on_warning$capture) { cnd <- sanitize_call(cnd) watcher$push(cnd) } if (on_warning$silence) { invokeRestart("muffleWarning") }}, error = function (cnd) { watcher$capture_plot_and_output() cnd <- sanitize_call(cnd) watcher$push(cnd) switch(on_error, continue = invokeRestart("eval_continue"), stop = invokeRestart("eval_stop"), error = NULL)})
28: eval(call)
29: eval(call)
30: with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers)
31: doWithOneRestart(return(expr), restart)
32: withOneRestart(expr, restarts[[1L]])
33: withRestartList(expr, restarts[-nr])
34: doWithOneRestart(return(expr), restart)
35: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
36: withRestartList(expr, restarts)
37: withRestarts(with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
38: evaluate::evaluate(...)
39: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options))
40: in_dir(input_dir(), expr)
41: in_input_dir(evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options)))
42: eng_r(options)
43: block_exec(params)
44: call_block(x)
45: process_group(group)
46: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) })
47: with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global()))
48: xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }), list(rlang_trace_top_env = knit_global())), function(loc) { setwd(wd) write_utf8(res, output %n% stdout()) paste0("\nQuitting from ", loc, if (!is.null(error)) paste0("\n", rule(), error, "\n", rule()))}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
49: process_file(text, output)
50: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
51: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output_dir = getwd(), ...)
52: vweave_rmarkdown(...)
53: engine$weave(file, quiet = quiet, encoding = enc)
54: doTryCatch(return(expr), name, parentenv, handler)
55: tryCatchOne(expr, names, parentenv, handlers[[1L]])
56: tryCatchList(expr, classes, parentenv, handlers)
57: tryCatch({ engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/Rtmp79XvSm/file86582cb28e6e.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from advent.Rmd:577-621 [oc-small]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'advent.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘advent.Rmd’
--- re-building ‘anthem-hfref.Rmd’ using rmarkdown
2026-09-28 08:59:45.660 R[37065:360939] XType: Using static font registry.
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = data_in$time[current_treatment], hazards = treatment_hazards, random_input = random_inputs$current_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)
2: fill_predictive_imputations(imputations = current, rows = current_treatment, values = impute_predictive_arm(time = data_in$time[current_treatment], hazards = treatment_hazards, random_input = random_inputs$current_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation))
3: impute_predictive_draws(data_in = data_interim, hazards = post_lambda, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, binary_imputation = predictive_binary_imputation, check_futility = check_futility)
4: evaluate_interim_decision(data_interim = data_interim, look = i, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, active_followup = active_followup_at(data_total, look_time), end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, method = method, binary_imputation = binary_imputation, check_futility = check_futility, Qn = Qn[i])
5: survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda,
Traceback:
1: lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, impute_predictive_arm(time = data_in$time[current_treatment], prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, hazards = treatment_hazards, random_input = random_inputs$current_treatment, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation) prop_loss = prop_loss, alternative = alternative, h0 = h0,
Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, 2: N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, fill_predictive_imputations(imputations = current, rows = current_treatment, return_trace = return_trace, prior_surv_final = prior_surv_final, values = impute_predictive_arm(time = data_in$time[current_treatment], generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) hazards = treatment_hazards, random_input = random_inputs$current_treatment,
end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)) 6:
doTryCatch(return(expr), name, parentenv, handler) 3:
impute_predictive_draws(data_in = data_interim, hazards = post_lambda, 7: end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, tryCatchOne(expr, names, parentenv, handlers[[1L]]) binary_imputation = predictive_binary_imputation, check_futility = check_futility)
8: 4: tryCatchList(expr, classes, parentenv, handlers)evaluate_interim_decision(data_interim = data_interim, look = i,
planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, 9: active_followup = active_followup_at(data_total, look_time), tryCatch({ end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, if (!is.null(trial_streams)) { single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, hazard_control = hazard_control, cutpoints = cutpoints, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, N_total = N_total, lambda = lambda, lambda_time = lambda_time, method = method, binary_imputation = binary_imputation, check_futility = check_futility, interim_look = interim_look, end_of_study = end_of_study, Qn = Qn[i]) prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method,
binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, 5: h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, cutpoints = cutpoints, N_total = N_total, lambda = lambda, method = method, imputed_final = imputed_final, empty_interval = empty_interval, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, return_trace = return_trace, prior_surv_final = prior_surv_final, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) attr(result, "arguments") <- NULL binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, if (inherits(result, "goldilocks_trial")) { prop_loss = prop_loss, alternative = alternative, h0 = h0, attr(result$summary, "arguments") <- NULL Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, } N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, list(trial = as.integer(x), result = result) imputed_final = imputed_final, empty_interval = empty_interval, }, error = function(error) { return_trace = return_trace, prior_surv_final = prior_surv_final, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) message = conditionMessage(error))
}) 6:
10: doTryCatch(return(expr), name, parentenv, handler)FUN(X[[i]], ...)
7: 11: tryCatchOne(expr, names, parentenv, handlers[[1L]])lapply(X = S, FUN = FUN, ...)
8: 12: tryCatchList(expr, classes, parentenv, handlers)doTryCatch(return(expr), name, parentenv, handler)
9: 13: tryCatch({tryCatchOne(expr, names, parentenv, handlers[[1L]]) if (!is.null(trial_streams)) {
assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv)14: }tryCatchList(expr, classes, parentenv, handlers) result <- survival_adapt_fn(hazard_treatment = hazard_treatment,
hazard_control = hazard_control, cutpoints = cutpoints, 15: N_total = N_total, lambda = lambda, lambda_time = lambda_time, tryCatch(expr, error = function(e) { interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, call <- conditionCall(e) binary_imputation = binary_imputation, block = block, if (!is.null(call)) { rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, if (identical(call[[1L]], quote(doTryCatch))) h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, call <- sys.call(-4L) N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, dcall <- deparse(call, nlines = 1L) method = method, imputed_final = imputed_final, empty_interval = empty_interval, prefix <- paste("Error in", dcall, ": ") return_trace = return_trace, prior_surv_final = prior_surv_final, LONG <- 75L generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) sm <- strsplit(conditionMessage(e), "\n")[[1L]] attr(result, "arguments") <- NULL w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (inherits(result, "goldilocks_trial")) { if (is.na(w)) attr(result$summary, "arguments") <- NULL w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], } type = "b") list(trial = as.integer(x), result = result) if (w > LONG) }, error = function(error) { prefix <- paste0(prefix, "\n ") list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], } message = conditionMessage(error)) else prefix <- "Error : "}) msg <- paste0(prefix, conditionMessage(e), "\n")
.Internal(seterrmessage(msg[1L]))10: if (!silent && isTRUE(getOption("show.error.messages"))) {FUN(X[[i]], ...) cat(msg, file = outFile)
.Internal(printDeferredWarnings())11: }lapply(X = S, FUN = FUN, ...) invisible(structure(msg, class = "try-error", condition = e))
})12:
doTryCatch(return(expr), name, parentenv, handler)16:
try(lapply(X = S, FUN = FUN, ...), silent = TRUE)13:
tryCatchOne(expr, names, parentenv, handlers[[1L]])17:
sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))14:
tryCatchList(expr, classes, parentenv, handlers)18:
FUN(X[[i]], ...)15:
tryCatch(expr, error = function(e) {19: call <- conditionCall(e)lapply(seq_len(cores), inner.do) if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch))) 20: call <- sys.call(-4L)mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, dcall <- deparse(call, nlines = 1L) mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive) prefix <- paste("Error in", dcall, ": ")
LONG <- 75L21: sm <- strsplit(conditionMessage(e), "\n")[[1L]]pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")
if (is.na(w)) 22: w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, type = "b") N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, if (w > LONG) end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, prefix <- paste0(prefix, "\n ") 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, } treatment = 1), prop_loss = 0, alternative = "greater", else prefix <- "Error : " h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, msg <- paste0(prefix, conditionMessage(e), "\n") N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", .Internal(seterrmessage(msg[1L])) imputed_final = FALSE, empty_interval = c("prior", "propagate", if (!silent && isTRUE(getOption("show.error.messages"))) { "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", cat(msg, file = outFile) "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", .Internal(printDeferredWarnings()) "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, } Qn = 1, rmst_tau = end_of_study) invisible(structure(msg, class = "try-error", condition = e)){}) Call <- match.call()
Arguments <- capture_arguments(sim_trials, environment())16: method <- normalize_analysis_method(method)try(lapply(X = S, FUN = FUN, ...), silent = TRUE) Arguments$method <- method
empty_interval <- match.arg(empty_interval)17: binary_imputation <- match.arg(binary_imputation)sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) backend <- match.arg(backend)
Arguments$empty_interval <- empty_interval18: Arguments$binary_imputation <- binary_imputationFUN(X[[i]], ...) Arguments$backend <- backend
requested_backend <- backend19: caller_rng_kind <- RNGkind()lapply(seq_len(cores), inner.do) validate_positive_integer_scalar(N_trials, "N_trials")
single_arm <- is.null(hazard_control)20: if (!single_arm) {mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, rand_ratio <- validate_randomization_args(N_total, block, mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive) rand_ratio, allocation_name = "rand_ratio")
Arguments$rand_ratio <- rand_ratio21: }pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers) prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm)
Arguments$prop_loss <- prop_loss22: validate_logical_scalar(return_trace, "return_trace")(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, validate_positive_integer_scalar(N_impute, "N_impute") N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, validate_positive_integer_scalar(N_mcmc, "N_mcmc") end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, validate_single_probability(mc_conf_level, "mc_conf_level", 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, upper_open = TRUE) treatment = 1), prop_loss = 0, alternative = "greater", if (mc_conf_level <= 0.5) { h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, stop("'mc_conf_level' must be greater than 0.5 and less than 1") } N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > imputed_final = FALSE, empty_interval = c("prior", "propagate", 0), N_impute = N_impute) "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", if (identical(method, "rmst")) { validate_endpoint_time(end_of_study, cutpoints, "end_of_study") "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", validate_rmst_args(rmst_tau, end_of_study, h0) "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, validate_analysis_configuration(method, alternative, Qn = 1, rmst_tau = end_of_study) is.null(hazard_control), imputed_final){ Call <- match.call() } Arguments <- capture_arguments(sim_trials, environment()) validate_positive_integer_scalar(ncores, "ncores") method <- normalize_analysis_method(method) execution <- resolve_sim_execution(backend = backend, ncores = ncores, Arguments$method <- method N_trials = N_trials) empty_interval <- match.arg(empty_interval) backend <- execution$backend binary_imputation <- match.arg(binary_imputation) workers <- execution$workers backend <- match.arg(backend) stream_seed <- NULL Arguments$empty_interval <- empty_interval Arguments$binary_imputation <- binary_imputation if (!is.null(seed)) { Arguments$backend <- backend if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || requested_backend <- backend !is.finite(seed) || seed != floor(seed)) { stop("'seed' must be NULL or a single integer value") caller_rng_kind <- RNGkind() } validate_positive_integer_scalar(N_trials, "N_trials") old_kind <- RNGkind() single_arm <- is.null(hazard_control) old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, if (!single_arm) { rand_ratio <- validate_randomization_args(N_total, block, rand_ratio, allocation_name = "rand_ratio") inherits = FALSE) Arguments$rand_ratio <- rand_ratio if (old_seed_exists) { } prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) old_seed <- get(".Random.seed", envir = .GlobalEnv, Arguments$prop_loss <- prop_loss inherits = FALSE) validate_logical_scalar(return_trace, "return_trace") } validate_positive_integer_scalar(N_impute, "N_impute") on.exit({ do.call(RNGkind, as.list(old_kind)) validate_positive_integer_scalar(N_mcmc, "N_mcmc") if (old_seed_exists) { assign(".Random.seed", old_seed, envir = .GlobalEnv) } else if (exists(".Random.seed", envir = .GlobalEnv, validate_single_probability(mc_conf_level, "mc_conf_level", upper_open = TRUE) inherits = FALSE)) { if (mc_conf_level <= 0.5) { rm(".Random.seed", envir = .GlobalEnv) } stop("'mc_conf_level' must be greater than 0.5 and less than 1") } }, add = TRUE) stream_seed <- seed validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > trial_streams <- make_rng_streams(stream_seed, N_trials) } 0), N_impute = N_impute) if (identical(method, "rmst")) { else if (backend == "psock") { validate_endpoint_time(end_of_study, cutpoints, "end_of_study") validate_rmst_args(rmst_tau, end_of_study, h0) stream_seed <- sample.int(.Machine$integer.max - 1L, size = 1L) validate_analysis_configuration(method, alternative, trial_streams <- make_rng_streams(stream_seed, N_trials) is.null(hazard_control), imputed_final) } } validate_positive_integer_scalar(ncores, "ncores") else { execution <- resolve_sim_execution(backend = backend, ncores = ncores, trial_streams <- NULL N_trials = N_trials) } rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { backend <- execution$backend caller_rng_kind[1] } else { workers <- execution$workers stream_seed <- NULL if (!is.null(seed)) { "L'Ecuyer-CMRG" if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || !is.finite(seed) || seed != floor(seed)) { }, seed_policy = if (!is.null(seed)) { "explicit_preserve_caller" stop("'seed' must be NULL or a single integer value") } } else if (backend == "psock") { old_kind <- RNGkind() "caller_derived_psock" old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, } else { inherits = FALSE) "caller_state" if (old_seed_exists) { }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), old_seed <- get(".Random.seed", envir = .GlobalEnv, stream_seed = stream_seed) survival_adapt_fn <- if (backend == "psock") { inherits = FALSE) } make_psock_callable("survival_adapt") on.exit({ } do.call(RNGkind, as.list(old_kind)) else { survival_adapt if (old_seed_exists) { } assign(".Random.seed", old_seed, envir = .GlobalEnv) } else if (exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) { survival_adapt_wrapper <- function(x) { rm(".Random.seed", envir = .GlobalEnv) } tryCatch({ if (!is.null(trial_streams)) { }, add = TRUE) stream_seed <- seed trial_streams <- make_rng_streams(stream_seed, N_trials) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } else if (backend == "psock") { } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, stream_seed <- sample.int(.Machine$integer.max - 1L, size = 1L) trial_streams <- make_rng_streams(stream_seed, N_trials) hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, } else { interim_look = interim_look, end_of_study = end_of_study, trial_streams <- NULL } prior_surv = prior_surv, prior_bin = prior_bin, rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, caller_rng_kind[1] alternative = alternative, h0 = h0, Fn = Fn, } else { "L'Ecuyer-CMRG" Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, }, seed_policy = if (!is.null(seed)) { method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, "explicit_preserve_caller" } else if (backend == "psock") { rmst_tau = rmst_tau) "caller_derived_psock" attr(result, "arguments") <- NULL } else { if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL } "caller_state" }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), stream_seed = stream_seed) survival_adapt_fn <- if (backend == "psock") { list(trial = as.integer(x), result = result) make_psock_callable("survival_adapt") }, error = function(error) { } list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error)) else { survival_adapt }) } } trial_index <- seq_len(N_trials) survival_adapt_wrapper <- function(x) { trial_results <- switch(backend, sequential = lapply(trial_index, tryCatch({ if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) survival_adapt_wrapper), fork = pbmclapply(trial_index, } survival_adapt_wrapper, mc.cores = workers), psock = { result <- survival_adapt_fn(hazard_treatment = hazard_treatment, active_cluster <- make_sim_cluster(workers) hazard_control = hazard_control, cutpoints = cutpoints, on.exit(stop_sim_cluster(active_cluster), add = TRUE) N_total = N_total, lambda = lambda, lambda_time = lambda_time, initialize_sim_cluster(active_cluster) interim_look = interim_look, end_of_study = end_of_study, run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) prior_surv = prior_surv, prior_bin = prior_bin, }) bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, failed <- vapply(trial_results, function(x) is.null(x$result), alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, logical(1)) N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, failed_results <- trial_results[failed] failures <- data.frame(trial = vapply(failed_results, `[[`, rmst_tau = rmst_tau) attr(result, "arguments") <- NULL integer(1), "trial"), error_class = vapply(failed_results, `[[`, character(1), "error_class"), message = vapply(failed_results, if (inherits(result, "goldilocks_trial")) { `[[`, character(1), "message"), stringsAsFactors = FALSE) attr(result$summary, "arguments") <- NULL if (all(failed)) { } error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", list(trial = as.integer(x), result = result) failures$message[1L])) }, error = function(error) { error$failures <- failures list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], class(error) <- c("goldilocks_all_trials_failed", class(error)) stop(error) } message = conditionMessage(error)) successful_trials <- trial_results[!failed] }) } successful_results <- lapply(successful_trials, `[[`, "result") trial_index <- seq_len(N_trials) if (return_trace) { trial_results <- switch(backend, sequential = lapply(trial_index, sims <- bind_rows(lapply(successful_results, function(x) x$summary)) survival_adapt_wrapper), fork = pbmclapply(trial_index, traces <- bind_rows(lapply(seq_along(successful_results), survival_adapt_wrapper, mc.cores = workers), psock = { function(i) { active_cluster <- make_sim_cluster(workers) trace <- successful_results[[i]]$trace on.exit(stop_sim_cluster(active_cluster), add = TRUE) trial <- successful_trials[[i]]$trial initialize_sim_cluster(active_cluster) trace$trial <- rep.int(trial, nrow(trace)) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) trace[c("trial", setdiff(names(trace), "trial"))] }) })) failed <- vapply(trial_results, function(x) is.null(x$result), out <- list(sims = sims, traces = traces, failures = failures, logical(1)) call = Call) failed_results <- trial_results[failed] } failures <- data.frame(trial = vapply(failed_results, `[[`, else { integer(1), "trial"), error_class = vapply(failed_results, sims <- bind_rows(successful_results) `[[`, character(1), "error_class"), message = vapply(failed_results, out <- list(sims = sims, failures = failures, call = Call) `[[`, character(1), "message"), stringsAsFactors = FALSE) } if (all(failed)) { attr(out$sims, "arguments") <- NULL error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, failures$message[1L])) N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, error$failures <- failures end_of_study = end_of_study) class(error) <- c("goldilocks_all_trials_failed", class(error)) attr(out, "decision_design") <- attr(successful_results[[1]], stop(error) "decision_design", exact = TRUE) } attr(out, "prior_design") <- attr(successful_results[[1]], successful_trials <- trial_results[!failed] "prior_design", exact = TRUE) successful_results <- lapply(successful_trials, `[[`, "result") attr(out, "rng_metadata") <- rng_metadata if (return_trace) { attr(out, "arguments") <- Arguments sims <- bind_rows(lapply(successful_results, function(x) x$summary)) attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, traces <- bind_rows(lapply(seq_along(successful_results), backend = backend, selection_reason = execution$reason, function(i) { requested_ncores = as.integer(ncores), workers = as.integer(workers), trace <- successful_results[[i]]$trace tasks = as.integer(N_trials)) trial <- successful_trials[[i]]$trial if (nrow(failures) > 0L) { trace$trial <- rep.int(trial, nrow(trace)) warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", trace[c("trial", setdiff(names(trace), "trial"))] "for details.", call. = FALSE) })) } out <- list(sims = sims, traces = traces, failures = failures, return(out) call = Call)})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, } lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, else { 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, sims <- bind_rows(successful_results) 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, out <- list(sims = sims, failures = failures, call = Call) prior_surv = c(1, 144.927536231884, 1, 144.927536231884, } 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, attr(out$sims, "arguments") <- NULL treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, end_of_study = end_of_study) mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", attr(out, "decision_design") <- attr(successful_results[[1]], imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, "decision_design", exact = TRUE) seed = 3425430) attr(out, "prior_design") <- attr(successful_results[[1]],
"prior_design", exact = TRUE)23: attr(out, "rng_metadata") <- rng_metadatado.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, attr(out, "arguments") <- Arguments hazard_control = hazard_control_week, N_trials = 20, ncores = 2, attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, seed = 3425430))) backend = backend, selection_reason = execution$reason,
requested_ncores = as.integer(ncores), workers = as.integer(workers), 24: tasks = as.integer(N_trials))eval(expr, envir) if (nrow(failures) > 0L) {
warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", 25: "for details.", call. = FALSE)eval(expr, envir) }
return(out)26: })(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, withVisible(eval(expr, envir)) lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333,
8.66666666666667, 13, 17.3333333333333, 21.6666666666667, 27: 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, withCallingHandlers(code, error = function (e) prior_surv = c(1, 144.927536231884, 1, 144.927536231884, rlang::entrace(e), message = function (cnd) 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, { treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, watcher$capture_plot_and_output() Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, if (on_message$capture) { 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, watcher$push(cnd) mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", } imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 if (on_message$silence) { ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, invokeRestart("muffleMessage") seed = 3425430) }
}, warning = function (cnd) 23: {do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, if (getOption("warn") >= 2 || getOption("warn") < 0) { hazard_control = hazard_control_week, N_trials = 20, ncores = 2, seed = 3425430))) return()
}24: watcher$capture_plot_and_output()eval(expr, envir) if (on_warning$capture) {
cnd <- sanitize_call(cnd)25: watcher$push(cnd)eval(expr, envir) }
if (on_warning$silence) {26: invokeRestart("muffleWarning")withVisible(eval(expr, envir)) }
}, error = function (cnd) 27: {withCallingHandlers(code, error = function (e) watcher$capture_plot_and_output()rlang::entrace(e), message = function (cnd) cnd <- sanitize_call(cnd){ watcher$push(cnd) watcher$capture_plot_and_output() switch(on_error, continue = invokeRestart("eval_continue"), if (on_message$capture) { stop = invokeRestart("eval_stop"), error = NULL) watcher$push(cnd)}) }
if (on_message$silence) {28: invokeRestart("muffleMessage")eval(call) }
}, warning = function (cnd) 29: {eval(call) if (getOption("warn") >= 2 || getOption("warn") < 0) {
return()30: }with_handlers({ watcher$capture_plot_and_output() for (expr in tle$exprs) { if (on_warning$capture) { ev <- withVisible(eval(expr, envir)) cnd <- sanitize_call(cnd) watcher$capture_plot_and_output() watcher$push(cnd) watcher$print_value(ev$value, ev$visible, envir) } } if (on_warning$silence) { TRUE invokeRestart("muffleWarning")}, handlers) }
}, error = function (cnd) 31: {doWithOneRestart(return(expr), restart) watcher$capture_plot_and_output()
cnd <- sanitize_call(cnd)32: watcher$push(cnd)withOneRestart(expr, restarts[[1L]]) switch(on_error, continue = invokeRestart("eval_continue"),
stop = invokeRestart("eval_stop"), error = NULL)33: })withRestartList(expr, restarts[-nr])
28: 34: eval(call)doWithOneRestart(return(expr), restart)
29: 35: eval(call)withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
30: 36: with_handlers({withRestartList(expr, restarts) for (expr in tle$exprs) {
ev <- withVisible(eval(expr, envir))37: watcher$capture_plot_and_output()withRestarts(with_handlers({ watcher$print_value(ev$value, ev$visible, envir) for (expr in tle$exprs) { } ev <- withVisible(eval(expr, envir)) TRUE watcher$capture_plot_and_output()}, handlers) watcher$print_value(ev$value, ev$visible, envir)
}31: TRUEdoWithOneRestart(return(expr), restart)}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
32: 38: withOneRestart(expr, restarts[[1L]])evaluate::evaluate(...)
33: 39: withRestartList(expr, restarts[-nr])evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message,
stop_on_error = if (is.numeric(options$error)) options$error else {34: if (options$error && options$include) doWithOneRestart(return(expr), restart) 0L
else 2L35: }, output_handler = knit_handlers(options$render, options))withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
40: 36: in_dir(input_dir(), expr)withRestartList(expr, restarts)
41: 37: in_input_dir(evaluate(code, envir = env, new_device = FALSE, withRestarts(with_handlers({ keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, for (expr in tle$exprs) { keep_message = if (is.numeric(options$message)) TRUE else options$message, ev <- withVisible(eval(expr, envir)) stop_on_error = if (is.numeric(options$error)) options$error else { watcher$capture_plot_and_output() if (options$error && options$include) watcher$print_value(ev$value, ev$visible, envir) 0L } else 2L TRUE }, output_handler = knit_handlers(options$render, options)))}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)
42: 38: eng_r(options)evaluate::evaluate(...)
43: 39: block_exec(params)evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning,
keep_message = if (is.numeric(options$message)) TRUE else options$message, 44: stop_on_error = if (is.numeric(options$error)) options$error else {call_block(x) if (options$error && options$include)
0L45: else 2Lprocess_group(group) }, output_handler = knit_handlers(options$render, options))
46: 40: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), in_dir(input_dir(), expr) error = function(e) {
if (progress && is.function(pb$interrupt)) 41: pb$interrupt()in_input_dir(evaluate(code, envir = env, new_device = FALSE, if (is_R_CMD_build() || is_R_CMD_check()) keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, error <<- format(e) keep_message = if (is.numeric(options$message)) TRUE else options$message, }) stop_on_error = if (is.numeric(options$error)) options$error else {
if (options$error && options$include) 47: 0Lwith_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { else 2L }, output_handler = knit_handlers(options$render, options)))
42: if (progress && is.function(pb$interrupt)) eng_r(options) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check())
error <<- format(e) }), list(rlang_trace_top_env = knit_global()))43: block_exec(params)
44: call_block(x)48:
xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 45: error = function(e) {process_group(group) if (progress && is.function(pb$interrupt))
pb$interrupt()46: if (is_R_CMD_build() || is_R_CMD_check()) withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error <<- format(e) error = function(e) { }), list(rlang_trace_top_env = knit_global())), function(loc) { if (progress && is.function(pb$interrupt)) setwd(wd) pb$interrupt() write_utf8(res, output %n% stdout()) if (is_R_CMD_build() || is_R_CMD_check()) paste0("\nQuitting from ", loc, if (!is.null(error)) error <<- format(e) paste0("\n", rule(), error, "\n", rule())) })}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
47: 49: with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), process_file(text, output) error = function(e) {
if (progress && is.function(pb$interrupt)) 50: pb$interrupt()knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet) if (is_R_CMD_build() || is_R_CMD_check())
error <<- format(e)51: }), list(rlang_trace_top_env = knit_global()))rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)48:
xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 52: error = function(e) {vweave_rmarkdown(...) if (progress && is.function(pb$interrupt))
pb$interrupt()53: if (is_R_CMD_build() || is_R_CMD_check()) engine$weave(file, quiet = quiet, encoding = enc) error <<- format(e)
}), list(rlang_trace_top_env = knit_global())), function(loc) {54: setwd(wd)doTryCatch(return(expr), name, parentenv, handler) write_utf8(res, output %n% stdout())
paste0("\nQuitting from ", loc, if (!is.null(error)) 55: paste0("\n", rule(), error, "\n", rule()))tryCatchOne(expr, names, parentenv, handlers[[1L]])}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
56: 49: tryCatchList(expr, classes, parentenv, handlers)process_file(text, output)
57: 50: tryCatch({knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet) engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)51: output <- find_vignette_product(name, by = "weave", engine = engine)rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), if (!have.makefile && vignette_is_tex(output)) { output_dir = getwd(), ...) texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf", 52: engine = engine)vweave_rmarkdown(...) }
}, error = function(e) {53: OK <<- FALSEengine$weave(file, quiet = quiet, encoding = enc) message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))54: })doTryCatch(return(expr), name, parentenv, handler)
58: 55: tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", tryCatchOne(expr, names, parentenv, handlers[[1L]]) TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/Rtmp79XvSm/file8658d74b1b4.rds")
56: An irrecoverable exception occurred. R is aborting now ...
tryCatchList(expr, classes, parentenv, handlers)
57: tryCatch({ engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/Rtmp79XvSm/file8658d74b1b4.rds")
An irrecoverable exception occurred. R is aborting now ...
Quitting from anthem-hfref.Rmd:432-482 [small-operating-characteristics]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `vapply()`:
! values must be length 1,
but FUN(X[[1]]) result is length 0
---
Backtrace:
▆
1. ├─base::do.call(...)
2. └─goldilocks (local) `<fn>`(...)
3. ├─base::data.frame(...)
4. └─base::vapply(failed_results, `[[`, integer(1), "trial")
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Error: processing vignette 'anthem-hfref.Rmd' failed with diagnostics:
values must be length 1,
but FUN(X[[1]]) result is length 0
--- failed re-building ‘anthem-hfref.Rmd’
--- re-building ‘architecture.Rmd’ using rmarkdown
--- finished re-building ‘architecture.Rmd’
--- re-building ‘bayes-piecewise.Rmd’ using rmarkdown
--- finished re-building ‘bayes-piecewise.Rmd’
--- re-building ‘bayesian-binary.Rmd’ using rmarkdown
--- finished re-building ‘bayesian-binary.Rmd’
--- re-building ‘calibrating-prob-ha.Rmd’ using rmarkdown
2026-09-28 08:59:51.757 R[37318:361731] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-09-28 08:59:53.576 R[38004:362988] XType: Using static font registry.
--- finished re-building ‘decision-traces.Rmd’
--- re-building ‘frequentist-binary.Rmd’ using rmarkdown
--- finished re-building ‘frequentist-binary.Rmd’
--- re-building ‘interim-data.Rmd’ using rmarkdown
--- finished re-building ‘interim-data.Rmd’
--- re-building ‘rmst.Rmd’ using rmarkdown
--- finished re-building ‘rmst.Rmd’
--- re-building ‘single-arm.Rmd’ using rmarkdown
--- finished re-building ‘single-arm.Rmd’
--- re-building ‘technical-methods.Rmd’ using rmarkdown
--- finished re-building ‘technical-methods.Rmd’
--- re-building ‘thermocool-af.Rmd’ using rmarkdown
2026-09-28 09:00:04.860 R[40505:367644] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-09-28 09:00:06.530 R[40593:367927] XType: Using static font registry.
--- finished re-building ‘two-arm.Rmd’
SUMMARY: processing the following files failed:
‘advent.Rmd’ ‘anthem-hfref.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-oldrel-macos-arm64
Current CRAN status: OK: 13
Current CRAN status: OK: 13