Last updated on 2026-10-23 05:56:04 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$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/Rtmp7C9ljM/file41e036505386.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/Rtmp7C9ljM/file41e036505386.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
2026-10-23 13:58:29.451 R[17928:343948] 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:
Traceback:
fill_predictive_imputations(imputations = current, rows = current_treatment, 1: values = impute_predictive_arm(time = data_in$time[current_treatment], impute_predictive_arm(time = data_in$time[current_treatment], hazards = treatment_hazards, random_input = random_inputs$current_treatment, hazards = treatment_hazards, random_input = random_inputs$current_treatment, end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)) end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)
3: 2: impute_predictive_draws(data_in = data_interim, hazards = post_lambda, fill_predictive_imputations(imputations = current, rows = current_treatment, end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, values = impute_predictive_arm(time = data_in$time[current_treatment], binary_imputation = predictive_binary_imputation, check_futility = check_futility) hazards = treatment_hazards, random_input = random_inputs$current_treatment,
end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)) 4:
evaluate_interim_decision(data_interim = data_interim, look = i, 3: planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, impute_predictive_draws(data_in = data_interim, hazards = post_lambda, active_followup = active_followup_at(data_total, look_time), end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, binary_imputation = predictive_binary_imputation, check_futility = check_futility) single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final,
prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, 4: h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, evaluate_interim_decision(data_interim = data_interim, look = i, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, method = method, binary_imputation = binary_imputation, check_futility = check_futility, active_followup = active_followup_at(data_total, look_time), Qn = Qn[i]) 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, 5: prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, cutpoints = cutpoints, N_total = N_total, lambda = lambda, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, method = method, binary_imputation = binary_imputation, check_futility = check_futility, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, Qn = Qn[i]) binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio,
prop_loss = prop_loss, alternative = alternative, h0 = h0, 5: Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, cutpoints = cutpoints, N_total = N_total, lambda = lambda, 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) 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, 6: N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, doTryCatch(return(expr), name, parentenv, handler) imputed_final = imputed_final, empty_interval = empty_interval,
return_trace = return_trace, prior_surv_final = prior_surv_final, 7: generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)tryCatchOne(expr, names, parentenv, handlers[[1L]])
6: 8: doTryCatch(return(expr), name, parentenv, handler)tryCatchList(expr, classes, parentenv, handlers)
7: 9: tryCatchOne(expr, names, parentenv, handlers[[1L]])tryCatch({
if (!is.null(trial_streams)) { 8: assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv)tryCatchList(expr, classes, parentenv, handlers) }
result <- survival_adapt_fn(hazard_treatment = hazard_treatment, 9: hazard_control = hazard_control, cutpoints = cutpoints, tryCatch({ N_total = N_total, lambda = lambda, lambda_time = lambda_time, if (!is.null(trial_streams)) { interim_look = interim_look, end_of_study = end_of_study, assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, } binary_imputation = binary_imputation, block = block, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, hazard_control = hazard_control, cutpoints = cutpoints, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_total = N_total, lambda = lambda, lambda_time = lambda_time, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, interim_look = interim_look, end_of_study = end_of_study, method = method, imputed_final = imputed_final, empty_interval = empty_interval, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, return_trace = return_trace, prior_surv_final = prior_surv_final, binary_imputation = binary_imputation, block = block, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, attr(result, "arguments") <- NULL h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, if (inherits(result, "goldilocks_trial")) { N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, attr(result$summary, "arguments") <- NULL method = method, imputed_final = imputed_final, empty_interval = empty_interval, } return_trace = return_trace, prior_surv_final = prior_surv_final, list(trial = as.integer(x), result = result) generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)}, error = function(error) { attr(result, "arguments") <- NULL list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], if (inherits(result, "goldilocks_trial")) { message = conditionMessage(error)) attr(result$summary, "arguments") <- NULL}) }
list(trial = as.integer(x), result = result)10: }, error = function(error) {FUN(X[[i]], ...) list(trial = as.integer(x), result = NULL, error_class = class(error)[1L],
message = conditionMessage(error))11: })lapply(X = S, FUN = FUN, ...)
10: 12: FUN(X[[i]], ...)doTryCatch(return(expr), name, parentenv, handler)
11: 13: lapply(X = S, FUN = FUN, ...)tryCatchOne(expr, names, parentenv, handlers[[1L]])
12: 14: doTryCatch(return(expr), name, parentenv, handler)tryCatchList(expr, classes, parentenv, handlers)
13: 15: tryCatchOne(expr, names, parentenv, handlers[[1L]])tryCatch(expr, error = function(e) {
call <- conditionCall(e)14: if (!is.null(call)) {tryCatchList(expr, classes, parentenv, handlers) if (identical(call[[1L]], quote(doTryCatch)))
15: call <- sys.call(-4L)tryCatch(expr, error = function(e) { dcall <- deparse(call, nlines = 1L) call <- conditionCall(e) prefix <- paste("Error in", dcall, ": ") if (!is.null(call)) { LONG <- 75L if (identical(call[[1L]], quote(doTryCatch))) sm <- strsplit(conditionMessage(e), "\n")[[1L]] call <- sys.call(-4L) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") dcall <- deparse(call, nlines = 1L) if (is.na(w)) prefix <- paste("Error in", dcall, ": ") w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], LONG <- 75L type = "b") sm <- strsplit(conditionMessage(e), "\n")[[1L]] if (w > LONG) w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") prefix <- paste0(prefix, "\n ") if (is.na(w)) } w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], else prefix <- "Error : " type = "b") if (w > LONG) msg <- paste0(prefix, conditionMessage(e), "\n") prefix <- paste0(prefix, "\n ") .Internal(seterrmessage(msg[1L])) } if (!silent && isTRUE(getOption("show.error.messages"))) { else prefix <- "Error : " cat(msg, file = outFile) msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(printDeferredWarnings()) .Internal(seterrmessage(msg[1L])) } if (!silent && isTRUE(getOption("show.error.messages"))) { invisible(structure(msg, class = "try-error", condition = e)) cat(msg, file = outFile)}) .Internal(printDeferredWarnings())
}16: invisible(structure(msg, class = "try-error", condition = e))try(lapply(X = S, FUN = FUN, ...), silent = TRUE)})
17: 16: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
18: 17: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))FUN(X[[i]], ...)
18: 19: FUN(X[[i]], ...)lapply(seq_len(cores), inner.do)
19: 20: lapply(seq_len(cores), inner.do)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)20:
mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, 21: mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive)pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)
21: 22: pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)(function (hazard_treatment, hazard_control = NULL, cutpoints = NULL,
N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, 22: 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, (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, treatment = 1), prop_loss = 0, alternative = "greater", N_total, lambda = 0.3, lambda_time = NULL, interim_look = NULL, h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, end_of_study, prior_surv = c(0.1, 0.1), prior_bin = c(1, N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, imputed_final = FALSE, empty_interval = c("prior", "propagate", treatment = 1), prop_loss = 0, alternative = "greater", "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", Qn = 1, rmst_tau = end_of_study) imputed_final = FALSE, empty_interval = c("prior", "propagate", { "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", Call <- match.call() "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", Arguments <- capture_arguments(sim_trials, environment()) "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, method <- normalize_analysis_method(method) Qn = 1, rmst_tau = end_of_study) Arguments$method <- method{ empty_interval <- match.arg(empty_interval) binary_imputation <- match.arg(binary_imputation) Call <- match.call() backend <- match.arg(backend) Arguments <- capture_arguments(sim_trials, environment()) Arguments$empty_interval <- empty_interval method <- normalize_analysis_method(method) Arguments$binary_imputation <- binary_imputation Arguments$method <- method Arguments$backend <- backend empty_interval <- match.arg(empty_interval) requested_backend <- backend caller_rng_kind <- RNGkind() binary_imputation <- match.arg(binary_imputation) validate_positive_integer_scalar(N_trials, "N_trials") backend <- match.arg(backend) single_arm <- is.null(hazard_control) Arguments$empty_interval <- empty_interval if (!single_arm) { Arguments$binary_imputation <- binary_imputation rand_ratio <- validate_randomization_args(N_total, block, Arguments$backend <- backend rand_ratio, allocation_name = "rand_ratio") requested_backend <- backend Arguments$rand_ratio <- rand_ratio caller_rng_kind <- RNGkind() } validate_positive_integer_scalar(N_trials, "N_trials") prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) single_arm <- is.null(hazard_control) Arguments$prop_loss <- prop_loss if (!single_arm) { validate_logical_scalar(return_trace, "return_trace") rand_ratio <- validate_randomization_args(N_total, block, validate_positive_integer_scalar(N_impute, "N_impute") rand_ratio, allocation_name = "rand_ratio") validate_positive_integer_scalar(N_mcmc, "N_mcmc") Arguments$rand_ratio <- rand_ratio validate_single_probability(mc_conf_level, "mc_conf_level", } upper_open = TRUE) prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) if (mc_conf_level <= 0.5) { Arguments$prop_loss <- prop_loss validate_logical_scalar(return_trace, "return_trace") stop("'mc_conf_level' must be greater than 0.5 and less than 1") validate_positive_integer_scalar(N_impute, "N_impute") } validate_positive_integer_scalar(N_mcmc, "N_mcmc") validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > validate_single_probability(mc_conf_level, "mc_conf_level", 0), N_impute = N_impute) upper_open = TRUE) if (identical(method, "rmst")) { if (mc_conf_level <= 0.5) { validate_endpoint_time(end_of_study, cutpoints, "end_of_study") stop("'mc_conf_level' must be greater than 0.5 and less than 1") validate_rmst_args(rmst_tau, end_of_study, h0) } validate_analysis_configuration(method, alternative, validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > is.null(hazard_control), imputed_final) 0), N_impute = N_impute) } if (identical(method, "rmst")) { validate_positive_integer_scalar(ncores, "ncores") validate_endpoint_time(end_of_study, cutpoints, "end_of_study") execution <- resolve_sim_execution(backend = backend, ncores = ncores, validate_rmst_args(rmst_tau, end_of_study, h0) N_trials = N_trials) validate_analysis_configuration(method, alternative, backend <- execution$backend is.null(hazard_control), imputed_final) workers <- execution$workers } stream_seed <- NULL validate_positive_integer_scalar(ncores, "ncores") if (!is.null(seed)) { execution <- resolve_sim_execution(backend = backend, ncores = ncores, if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || N_trials = N_trials) !is.finite(seed) || seed != floor(seed)) { backend <- execution$backend stop("'seed' must be NULL or a single integer value") workers <- execution$workers } stream_seed <- NULL old_kind <- RNGkind() if (!is.null(seed)) { old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || inherits = FALSE) !is.finite(seed) || seed != floor(seed)) { if (old_seed_exists) { stop("'seed' must be NULL or a single integer value") old_seed <- get(".Random.seed", envir = .GlobalEnv, } inherits = FALSE) old_kind <- RNGkind() } old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, on.exit({ inherits = FALSE) do.call(RNGkind, as.list(old_kind)) if (old_seed_exists) { if (old_seed_exists) { old_seed <- get(".Random.seed", envir = .GlobalEnv, assign(".Random.seed", old_seed, envir = .GlobalEnv) inherits = FALSE) } else if (exists(".Random.seed", envir = .GlobalEnv, } inherits = FALSE)) { on.exit({ rm(".Random.seed", envir = .GlobalEnv) do.call(RNGkind, as.list(old_kind)) } if (old_seed_exists) { }, add = TRUE) assign(".Random.seed", old_seed, envir = .GlobalEnv) stream_seed <- seed } else if (exists(".Random.seed", envir = .GlobalEnv, trial_streams <- make_rng_streams(stream_seed, N_trials) inherits = FALSE)) { } rm(".Random.seed", envir = .GlobalEnv) else if (backend == "psock") { } stream_seed <- sample.int(.Machine$integer.max - 1L, }, add = TRUE) size = 1L) stream_seed <- seed trial_streams <- make_rng_streams(stream_seed, N_trials) trial_streams <- make_rng_streams(stream_seed, N_trials) } } else { else if (backend == "psock") { trial_streams <- NULL } stream_seed <- sample.int(.Machine$integer.max - 1L, rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { size = 1L) caller_rng_kind[1] trial_streams <- make_rng_streams(stream_seed, N_trials) } else { "L'Ecuyer-CMRG" } }, seed_policy = if (!is.null(seed)) { else { "explicit_preserve_caller" trial_streams <- NULL } else if (backend == "psock") { } "caller_derived_psock" rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { } else { caller_rng_kind[1] "caller_state" } else { }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), "L'Ecuyer-CMRG" stream_seed = stream_seed) }, seed_policy = if (!is.null(seed)) { survival_adapt_fn <- if (backend == "psock") { "explicit_preserve_caller" make_psock_callable("survival_adapt") } else if (backend == "psock") { } "caller_derived_psock" else { } else { survival_adapt "caller_state" } }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), survival_adapt_wrapper <- function(x) { stream_seed = stream_seed) tryCatch({ survival_adapt_fn <- if (backend == "psock") { if (!is.null(trial_streams)) { make_psock_callable("survival_adapt") assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } } else { result <- survival_adapt_fn(hazard_treatment = hazard_treatment, survival_adapt hazard_control = hazard_control, cutpoints = cutpoints, } N_total = N_total, lambda = lambda, lambda_time = lambda_time, survival_adapt_wrapper <- function(x) { interim_look = interim_look, end_of_study = end_of_study, tryCatch({ prior_surv = prior_surv, prior_bin = prior_bin, if (!is.null(trial_streams)) { bin_method = bin_method, binary_imputation = binary_imputation, assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, } alternative = alternative, h0 = h0, Fn = Fn, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, hazard_control = hazard_control, cutpoints = cutpoints, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, N_total = N_total, lambda = lambda, lambda_time = lambda_time, method = method, imputed_final = imputed_final, interim_look = interim_look, end_of_study = end_of_study, empty_interval = empty_interval, return_trace = return_trace, prior_surv = prior_surv, prior_bin = prior_bin, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, bin_method = bin_method, binary_imputation = binary_imputation, rmst_tau = rmst_tau) block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, attr(result, "arguments") <- NULL alternative = alternative, h0 = h0, Fn = Fn, if (inherits(result, "goldilocks_trial")) { Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, attr(result$summary, "arguments") <- NULL N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, } method = method, imputed_final = imputed_final, list(trial = as.integer(x), result = result) empty_interval = empty_interval, return_trace = return_trace, }, error = function(error) { prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], rmst_tau = rmst_tau) message = conditionMessage(error)) attr(result, "arguments") <- NULL }) if (inherits(result, "goldilocks_trial")) { } attr(result$summary, "arguments") <- NULL trial_index <- seq_len(N_trials) } trial_results <- switch(backend, sequential = lapply(trial_index, list(trial = as.integer(x), result = result) survival_adapt_wrapper), fork = pbmclapply(trial_index, }, error = function(error) { survival_adapt_wrapper, mc.cores = workers), psock = { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], active_cluster <- make_sim_cluster(workers) message = conditionMessage(error)) on.exit(stop_sim_cluster(active_cluster), add = TRUE) }) initialize_sim_cluster(active_cluster) } trial_index <- seq_len(N_trials) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) 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) failed <- vapply(trial_results, function(x) is.null(x$result), on.exit(stop_sim_cluster(active_cluster), add = TRUE) initialize_sim_cluster(active_cluster) logical(1)) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) failed_results <- trial_results[failed] }) failures <- data.frame(trial = vapply(failed_results, `[[`, integer(1), "trial"), error_class = vapply(failed_results, failed <- vapply(trial_results, function(x) is.null(x$result), `[[`, character(1), "error_class"), message = vapply(failed_results, logical(1)) `[[`, character(1), "message"), stringsAsFactors = FALSE) failed_results <- trial_results[failed] if (all(failed)) { error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", failures <- data.frame(trial = vapply(failed_results, `[[`, failures$message[1L])) integer(1), "trial"), error_class = vapply(failed_results, error$failures <- failures `[[`, character(1), "error_class"), message = vapply(failed_results, class(error) <- c("goldilocks_all_trials_failed", class(error)) `[[`, character(1), "message"), stringsAsFactors = FALSE) stop(error) if (all(failed)) { } error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", successful_trials <- trial_results[!failed] failures$message[1L])) successful_results <- lapply(successful_trials, `[[`, "result") error$failures <- failures if (return_trace) { class(error) <- c("goldilocks_all_trials_failed", class(error)) sims <- bind_rows(lapply(successful_results, function(x) x$summary)) stop(error) traces <- bind_rows(lapply(seq_along(successful_results), function(i) { } trace <- successful_results[[i]]$trace successful_trials <- trial_results[!failed] trial <- successful_trials[[i]]$trial successful_results <- lapply(successful_trials, `[[`, "result") trace$trial <- rep.int(trial, nrow(trace)) if (return_trace) { trace[c("trial", setdiff(names(trace), "trial"))] sims <- bind_rows(lapply(successful_results, function(x) x$summary)) })) traces <- bind_rows(lapply(seq_along(successful_results), out <- list(sims = sims, traces = traces, failures = failures, function(i) { call = Call) trace <- successful_results[[i]]$trace } trial <- successful_trials[[i]]$trial else { trace$trial <- rep.int(trial, nrow(trace)) sims <- bind_rows(successful_results) trace[c("trial", setdiff(names(trace), "trial"))] out <- list(sims = sims, failures = failures, call = Call) })) } out <- list(sims = sims, traces = traces, failures = failures, attr(out$sims, "arguments") <- NULL call = Call) attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, } N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, else { end_of_study = end_of_study) sims <- bind_rows(successful_results) attr(out, "decision_design") <- attr(successful_results[[1]], out <- list(sims = sims, failures = failures, call = Call) "decision_design", exact = TRUE) } attr(out, "prior_design") <- attr(successful_results[[1]], attr(out$sims, "arguments") <- NULL "prior_design", exact = TRUE) attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, attr(out, "rng_metadata") <- rng_metadata end_of_study = end_of_study) attr(out, "arguments") <- Arguments attr(out, "decision_design") <- attr(successful_results[[1]], attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, "decision_design", exact = TRUE) backend = backend, selection_reason = execution$reason, attr(out, "prior_design") <- attr(successful_results[[1]], requested_ncores = as.integer(ncores), workers = as.integer(workers), "prior_design", exact = TRUE) tasks = as.integer(N_trials)) attr(out, "rng_metadata") <- rng_metadata if (nrow(failures) > 0L) { attr(out, "arguments") <- Arguments warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, "for details.", call. = FALSE) backend = backend, selection_reason = execution$reason, } requested_ncores = as.integer(ncores), workers = as.integer(workers), return(out) tasks = as.integer(N_trials))})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, if (nrow(failures) > 0L) { lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, "for details.", call. = FALSE) 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, } prior_surv = c(1, 144.927536231884, 1, 144.927536231884, return(out) 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, })(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, lambda = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6), lambda_time = c(4.33333333333333, Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", prior_surv = c(1, 144.927536231884, 1, 144.927536231884, imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, seed = 3425430) Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95,
0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, 23: mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 hazard_control = hazard_control_week, N_trials = 20, ncores = 2, ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, seed = 3425430))) seed = 3425430)
24: 23: eval(expr, envir)do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week,
hazard_control = hazard_control_week, N_trials = 20, ncores = 2, 25: seed = 3425430)))eval(expr, envir)
24: 26: withVisible(eval(expr, envir))eval(expr, envir)
27: 25: withCallingHandlers(code, error = function (e) eval(expr, envir)rlang::entrace(e), message = function (cnd)
{26: watcher$capture_plot_and_output()withVisible(eval(expr, envir)) if (on_message$capture) {
27: watcher$push(cnd)withCallingHandlers(code, error = function (e) }rlang::entrace(e), message = function (cnd) if (on_message$silence) {{ invokeRestart("muffleMessage") watcher$capture_plot_and_output() } if (on_message$capture) {}, warning = function (cnd) watcher$push(cnd){ } if (getOption("warn") >= 2 || getOption("warn") < 0) { if (on_message$silence) { return() invokeRestart("muffleMessage") } } watcher$capture_plot_and_output()}, warning = function (cnd) if (on_warning$capture) {{ if (getOption("warn") >= 2 || getOption("warn") < 0) { cnd <- sanitize_call(cnd) return() watcher$push(cnd) } } watcher$capture_plot_and_output() if (on_warning$silence) { if (on_warning$capture) { invokeRestart("muffleWarning") cnd <- sanitize_call(cnd) } watcher$push(cnd)}, error = function (cnd) }{ if (on_warning$silence) { watcher$capture_plot_and_output() invokeRestart("muffleWarning") cnd <- sanitize_call(cnd) } watcher$push(cnd)}, error = function (cnd) switch(on_error, continue = invokeRestart("eval_continue"), { watcher$capture_plot_and_output() stop = invokeRestart("eval_stop"), error = NULL) cnd <- sanitize_call(cnd)}) watcher$push(cnd)
switch(on_error, continue = invokeRestart("eval_continue"), 28: stop = invokeRestart("eval_stop"), error = NULL)eval(call)})
29: 28: eval(call)eval(call)
30: 29: with_handlers({eval(call) for (expr in tle$exprs) {
ev <- withVisible(eval(expr, envir))30: watcher$capture_plot_and_output()with_handlers({ watcher$print_value(ev$value, ev$visible, envir) for (expr in tle$exprs) { } TRUE ev <- withVisible(eval(expr, envir))}, handlers) watcher$capture_plot_and_output()
watcher$print_value(ev$value, ev$visible, envir)31: }doWithOneRestart(return(expr), restart) TRUE
}, handlers)32:
withOneRestart(expr, restarts[[1L]])31:
doWithOneRestart(return(expr), restart)33:
withRestartList(expr, restarts[-nr])32:
withOneRestart(expr, restarts[[1L]])34:
doWithOneRestart(return(expr), restart)33:
withRestartList(expr, restarts[-nr])35:
withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
34: 36: doWithOneRestart(return(expr), restart)withRestartList(expr, restarts)
35: 37: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])withRestarts(with_handlers({
for (expr in tle$exprs) {36: ev <- withVisible(eval(expr, envir))withRestartList(expr, restarts) watcher$capture_plot_and_output()
watcher$print_value(ev$value, ev$visible, envir)37: }withRestarts(with_handlers({ TRUE for (expr in tle$exprs) {}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE) ev <- withVisible(eval(expr, envir))
watcher$capture_plot_and_output()38: watcher$print_value(ev$value, ev$visible, envir)evaluate::evaluate(...) }
TRUE39: }, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)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, 38: stop_on_error = if (is.numeric(options$error)) options$error else {evaluate::evaluate(...) if (options$error && options$include)
0L39: else 2Levaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, }, output_handler = knit_handlers(options$render, options)) keep_message = if (is.numeric(options$message)) TRUE else options$message,
stop_on_error = if (is.numeric(options$error)) options$error else {40: if (options$error && options$include) in_dir(input_dir(), expr) 0L else 2L
}, output_handler = knit_handlers(options$render, options))
41: 40: in_input_dir(evaluate(code, envir = env, new_device = FALSE, in_dir(input_dir(), expr) keep_warning = if (is.numeric(options$warning)) TRUE else options$warning,
keep_message = if (is.numeric(options$message)) TRUE else options$message, 41: stop_on_error = if (is.numeric(options$error)) options$error else {in_input_dir(evaluate(code, envir = env, new_device = FALSE, if (options$error && options$include) 0L keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, else 2L stop_on_error = if (is.numeric(options$error)) options$error else { }, output_handler = knit_handlers(options$render, options))) if (options$error && options$include) 0L
else 2L42: eng_r(options) }, output_handler = knit_handlers(options$render, options)))
43:
block_exec(params)42:
eng_r(options)44:
call_block(x)43:
block_exec(params)45:
process_group(group)44:
call_block(x)46:
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() if (is_R_CMD_build() || is_R_CMD_check())
error <<- format(e)46: })withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {47: if (progress && is.function(pb$interrupt)) with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), pb$interrupt() error = function(e) { if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) if (progress && is.function(pb$interrupt)) })
pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) 47: error <<- format(e)with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), }), list(rlang_trace_top_env = knit_global())) error = function(e) {
if (progress && is.function(pb$interrupt)) 48: pb$interrupt()xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), if (is_R_CMD_build() || is_R_CMD_check()) error = function(e) { error <<- format(e) if (progress && is.function(pb$interrupt)) }), list(rlang_trace_top_env = knit_global())) pb$interrupt()
if (is_R_CMD_build() || is_R_CMD_check()) 48: error <<- format(e)xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), }), list(rlang_trace_top_env = knit_global())), function(loc) { error = function(e) { setwd(wd) if (progress && is.function(pb$interrupt)) write_utf8(res, output %n% stdout()) pb$interrupt() paste0("\nQuitting from ", loc, if (!is.null(error)) if (is_R_CMD_build() || is_R_CMD_check()) paste0("\n", rule(), error, "\n", rule())) error <<- format(e)}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc) }), list(rlang_trace_top_env = knit_global())), function(loc) {
setwd(wd)49: write_utf8(res, output %n% stdout()) paste0("\nQuitting from ", loc, if (!is.null(error)) process_file(text, output)
paste0("\n", rule(), error, "\n", rule()))}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)50: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
49: 51: process_file(text, output)rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
50: output_dir = getwd(), ...)knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
52: 51: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), vweave_rmarkdown(...) output_dir = getwd(), ...)
53: 52: engine$weave(file, quiet = quiet, encoding = enc)vweave_rmarkdown(...)
54: doTryCatch(return(expr), name, parentenv, handler)53:
engine$weave(file, quiet = quiet, encoding = enc)55: tryCatchOne(expr, names, parentenv, handlers[[1L]])
54: 56: tryCatchList(expr, classes, parentenv, handlers)doTryCatch(return(expr), name, parentenv, handler)
57: tryCatch({55: engine$weave(file, quiet = quiet, encoding = enc)tryCatchOne(expr, names, parentenv, handlers[[1L]]) setwd(startdir)
56: output <- find_vignette_product(name, by = "weave", engine = engine)tryCatchList(expr, classes, parentenv, handlers) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf", 57: engine = engine)tryCatch({ } engine$weave(file, quiet = quiet, encoding = enc)}, error = function(e) { OK <<- FALSE setwd(startdir) message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e))) output <- find_vignette_product(name, by = "weave", engine = engine)})
if (!have.makefile && vignette_is_tex(output)) {58: texi2pdf(file = output, clean = FALSE, quiet = quiet)tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", output <- find_vignette_product(name, by = "texi2pdf", TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/Rtmp7C9ljM/file41e02009036a.rds")
engine = engine)An irrecoverable exception occurred. R is aborting now ...
}}, 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/Rtmp7C9ljM/file41e02009036a.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
2026-10-23 13:58:35.875 R[19746:346810] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-10-23 13:58:37.577 R[20607:348101] 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
2026-10-23 13:58:49.034 R[21647:350232] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-10-23 13:58:50.952 R[22130:351215] 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