Last updated on 2026-10-06 05:55:11 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$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
58: tools:::.buildOneVignette("advent.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", TRUE, TRUE, "advent", "UTF-8", "/Volumes/Temp/tmp/RtmpFUQquN/file6e0962d3a35b.rds")
An irrecoverable exception occurred. R is aborting now ...
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: impute_predictive_arm(time = rep.int(0, length(future_treatment)), hazards = treatment_hazards, random_input = random_inputs$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/RtmpFUQquN/file6e0962d3a35b.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
2026-10-06 12:53:19.334 R[32535:346855] XType: Using static font registry.
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1:
Traceback:
impute_predictive_arm(time = data_in$time[current_treatment], 1: hazards = treatment_hazards, random_input = random_inputs$current_treatment, impute_predictive_arm(time = data_in$time[current_treatment], end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation) hazards = treatment_hazards, random_input = random_inputs$current_treatment,
end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation) 2:
fill_predictive_imputations(imputations = current, rows = current_treatment, 2: values = impute_predictive_arm(time = data_in$time[current_treatment], fill_predictive_imputations(imputations = current, rows = current_treatment, hazards = treatment_hazards, random_input = random_inputs$current_treatment, values = impute_predictive_arm(time = data_in$time[current_treatment], end_of_study = end_of_study, cutpoints = cutpoints, binary_imputation = binary_imputation)) hazards = treatment_hazards, random_input = random_inputs$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, 3: end_of_study = end_of_study, cutpoints = cutpoints, single_arm = single_arm, impute_predictive_draws(data_in = data_interim, hazards = post_lambda, binary_imputation = predictive_binary_imputation, check_futility = check_futility) 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, 4: planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, evaluate_interim_decision(data_interim = data_interim, look = i, active_followup = active_followup_at(data_total, look_time), planned_N = analysis_at_enrollnumber[i], calendar_time = look_time, end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, active_followup = active_followup_at(data_total, look_time), single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, end_of_study = end_of_study, rmst_tau = rmst_tau, cutpoints = cutpoints, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, single_arm = single_arm, prior_surv = prior_surv, prior_surv_final = prior_surv_final, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, prior_bin = prior_bin, bin_method = bin_method, alternative = alternative, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, h0 = h0, Fn = Fn[i], Sn = Sn[i], prob_ha = prob_ha, N_impute = N_impute, method = method, binary_imputation = binary_imputation, check_futility = check_futility, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, empty_interval = empty_interval, Qn = Qn[i]) 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, 5: cutpoints = cutpoints, N_total = N_total, lambda = lambda, survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, cutpoints = cutpoints, N_total = N_total, lambda = lambda, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, 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, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, 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, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau)
6: 6: doTryCatch(return(expr), name, parentenv, handler)doTryCatch(return(expr), name, parentenv, handler)
7: 7: tryCatchOne(expr, names, parentenv, handlers[[1L]])tryCatchOne(expr, names, parentenv, handlers[[1L]])
8: 8: tryCatchList(expr, classes, parentenv, handlers)tryCatchList(expr, classes, parentenv, handlers)
9: 9: tryCatch({tryCatch({ if (!is.null(trial_streams)) { if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, block = block, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, 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, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, empty_interval = empty_interval, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) attr(result, "arguments") <- NULL attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) { if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL attr(result$summary, "arguments") <- NULL } } list(trial = as.integer(x), result = result) list(trial = as.integer(x), result = result)}, error = function(error) {}, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error)) message = conditionMessage(error))})})
10: 10: FUN(X[[i]], ...)FUN(X[[i]], ...)
11: 11: lapply(X = S, FUN = FUN, ...)lapply(X = S, FUN = FUN, ...)
12: 12: doTryCatch(return(expr), name, parentenv, handler)doTryCatch(return(expr), name, parentenv, handler)
13: 13: tryCatchOne(expr, names, parentenv, handlers[[1L]])tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: 14: tryCatchList(expr, classes, parentenv, handlers)tryCatchList(expr, classes, parentenv, handlers)
15: 15: tryCatch(expr, error = function(e) {tryCatch(expr, error = function(e) { call <- conditionCall(e) call <- conditionCall(e) if (!is.null(call)) { if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") prefix <- paste("Error in", dcall, ": ") LONG <- 75L LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") type = "b") if (w > LONG) if (w > LONG) prefix <- paste0(prefix, "\n ") prefix <- paste0(prefix, "\n ") } } else prefix <- "Error : " else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) cat(msg, file = outFile) .Internal(printDeferredWarnings()) .Internal(printDeferredWarnings()) } } invisible(structure(msg, class = "try-error", condition = e)) invisible(structure(msg, class = "try-error", condition = e))})})
16: 16: try(lapply(X = S, FUN = FUN, ...), silent = TRUE)try(lapply(X = S, FUN = FUN, ...), silent = TRUE)
17: 17: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
18: 18: FUN(X[[i]], ...)FUN(X[[i]], ...)
19: 19: lapply(seq_len(cores), inner.do)lapply(seq_len(cores), inner.do)
20: 20: mclapply(X, FUN, ..., mc.cores = mc.cores, mc.preschedule = mc.preschedule, 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) mc.set.seed = mc.set.seed, mc.cleanup = mc.cleanup, mc.allow.recursive = mc.allow.recursive)
21: 21: pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers)
22: 22: (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, (function (hazard_treatment, hazard_control = NULL, cutpoints = NULL, N_total, lambda = 0.3, lambda_time = NULL, interim_look = 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, 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, 1), bin_method = "mc", block = 2, rand_ratio = c(control = 1, treatment = 1), prop_loss = 0, alternative = "greater", treatment = 1), prop_loss = 0, alternative = "greater", h0 = 0, Fn = 0.05, Sn = 0.9, prob_ha = 0.95, N_impute = 500, 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", N_mcmc = 1000, mc_conf_level = 0.95, N_trials = 10, method = "logrank", imputed_final = FALSE, empty_interval = c("prior", "propagate", imputed_final = FALSE, empty_interval = c("prior", "propagate", "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", "error"), return_trace = FALSE, ncores = 1L, backend = c("auto", "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", "fork", "psock", "sequential"), seed = NULL, binary_imputation = c("event-time", "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, "bernoulli"), prior_surv_final = prior_surv, generation_cutpoints = cutpoints, Qn = 1, rmst_tau = end_of_study) Qn = 1, rmst_tau = end_of_study) {{ Call <- match.call() Call <- match.call() Arguments <- capture_arguments(sim_trials, environment()) Arguments <- capture_arguments(sim_trials, environment()) method <- normalize_analysis_method(method) method <- normalize_analysis_method(method) Arguments$method <- method Arguments$method <- method empty_interval <- match.arg(empty_interval) empty_interval <- match.arg(empty_interval) binary_imputation <- match.arg(binary_imputation) binary_imputation <- match.arg(binary_imputation) backend <- match.arg(backend) backend <- match.arg(backend) Arguments$empty_interval <- empty_interval Arguments$empty_interval <- empty_interval Arguments$binary_imputation <- binary_imputation Arguments$binary_imputation <- binary_imputation Arguments$backend <- backend Arguments$backend <- backend requested_backend <- backend requested_backend <- backend caller_rng_kind <- RNGkind() caller_rng_kind <- RNGkind() validate_positive_integer_scalar(N_trials, "N_trials") validate_positive_integer_scalar(N_trials, "N_trials") single_arm <- is.null(hazard_control) single_arm <- is.null(hazard_control) if (!single_arm) { if (!single_arm) { rand_ratio <- validate_randomization_args(N_total, block, rand_ratio <- validate_randomization_args(N_total, block, rand_ratio, allocation_name = "rand_ratio") rand_ratio, allocation_name = "rand_ratio") Arguments$rand_ratio <- rand_ratio Arguments$rand_ratio <- rand_ratio } } prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) prop_loss <- normalize_prop_loss(prop_loss, single_arm = single_arm) Arguments$prop_loss <- prop_loss Arguments$prop_loss <- prop_loss validate_logical_scalar(return_trace, "return_trace") validate_logical_scalar(return_trace, "return_trace") validate_positive_integer_scalar(N_impute, "N_impute") validate_positive_integer_scalar(N_impute, "N_impute") validate_positive_integer_scalar(N_mcmc, "N_mcmc") validate_positive_integer_scalar(N_mcmc, "N_mcmc") validate_single_probability(mc_conf_level, "mc_conf_level", validate_single_probability(mc_conf_level, "mc_conf_level", upper_open = TRUE) upper_open = TRUE) if (mc_conf_level <= 0.5) { if (mc_conf_level <= 0.5) { stop("'mc_conf_level' must be greater than 0.5 and less than 1") 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 > validate_final_imputation(method, imputed_final, has_missing_outcomes = any(prop_loss > 0), N_impute = N_impute) 0), N_impute = N_impute) if (identical(method, "rmst")) { if (identical(method, "rmst")) { validate_endpoint_time(end_of_study, cutpoints, "end_of_study") validate_endpoint_time(end_of_study, cutpoints, "end_of_study") validate_rmst_args(rmst_tau, end_of_study, h0) validate_rmst_args(rmst_tau, end_of_study, h0) validate_analysis_configuration(method, alternative, validate_analysis_configuration(method, alternative, is.null(hazard_control), imputed_final) is.null(hazard_control), imputed_final) } } validate_positive_integer_scalar(ncores, "ncores") validate_positive_integer_scalar(ncores, "ncores") execution <- resolve_sim_execution(backend = backend, ncores = ncores, execution <- resolve_sim_execution(backend = backend, ncores = ncores, N_trials = N_trials) N_trials = N_trials) backend <- execution$backend backend <- execution$backend workers <- execution$workers workers <- execution$workers stream_seed <- NULL stream_seed <- NULL if (!is.null(seed)) { if (!is.null(seed)) { if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || if (length(seed) != 1 || !is.numeric(seed) || is.na(seed) || !is.finite(seed) || seed != floor(seed)) { !is.finite(seed) || seed != floor(seed)) { stop("'seed' must be NULL or a single integer value") stop("'seed' must be NULL or a single integer value") } } old_kind <- RNGkind() old_kind <- RNGkind() old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, old_seed_exists <- exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE) inherits = FALSE) if (old_seed_exists) { if (old_seed_exists) { old_seed <- get(".Random.seed", envir = .GlobalEnv, old_seed <- get(".Random.seed", envir = .GlobalEnv, inherits = FALSE) inherits = FALSE) } } on.exit({ on.exit({ do.call(RNGkind, as.list(old_kind)) do.call(RNGkind, as.list(old_kind)) if (old_seed_exists) { if (old_seed_exists) { assign(".Random.seed", old_seed, envir = .GlobalEnv) assign(".Random.seed", old_seed, envir = .GlobalEnv) } else if (exists(".Random.seed", envir = .GlobalEnv, } else if (exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) { inherits = FALSE)) { rm(".Random.seed", envir = .GlobalEnv) rm(".Random.seed", envir = .GlobalEnv) } } }, add = TRUE) }, add = TRUE) stream_seed <- seed stream_seed <- seed trial_streams <- make_rng_streams(stream_seed, N_trials) trial_streams <- make_rng_streams(stream_seed, N_trials) } } else if (backend == "psock") { else if (backend == "psock") { stream_seed <- sample.int(.Machine$integer.max - 1L, stream_seed <- sample.int(.Machine$integer.max - 1L, size = 1L) size = 1L) trial_streams <- make_rng_streams(stream_seed, N_trials) trial_streams <- make_rng_streams(stream_seed, N_trials) } } else { else { trial_streams <- NULL trial_streams <- NULL } } rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { rng_metadata <- list(caller_kind = caller_rng_kind, stream_kind = if (is.null(trial_streams)) { caller_rng_kind[1] caller_rng_kind[1] } else { } else { "L'Ecuyer-CMRG" "L'Ecuyer-CMRG" }, seed_policy = if (!is.null(seed)) { }, seed_policy = if (!is.null(seed)) { "explicit_preserve_caller" "explicit_preserve_caller" } else if (backend == "psock") { } else if (backend == "psock") { "caller_derived_psock" "caller_derived_psock" } else { } else { "caller_state" "caller_state" }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), }, backend = backend, ncores = as.integer(workers), requested_ncores = as.integer(ncores), stream_seed = stream_seed) stream_seed = stream_seed) survival_adapt_fn <- if (backend == "psock") { survival_adapt_fn <- if (backend == "psock") { make_psock_callable("survival_adapt") make_psock_callable("survival_adapt") } } else { else { survival_adapt survival_adapt } } survival_adapt_wrapper <- function(x) { survival_adapt_wrapper <- function(x) { tryCatch({ tryCatch({ if (!is.null(trial_streams)) { if (!is.null(trial_streams)) { assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) assign(".Random.seed", trial_streams[[x]], envir = .GlobalEnv) } } result <- survival_adapt_fn(hazard_treatment = hazard_treatment, result <- survival_adapt_fn(hazard_treatment = hazard_treatment, hazard_control = hazard_control, cutpoints = cutpoints, hazard_control = hazard_control, cutpoints = cutpoints, N_total = N_total, lambda = lambda, lambda_time = lambda_time, N_total = N_total, lambda = lambda, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study, interim_look = interim_look, end_of_study = end_of_study, prior_surv = prior_surv, prior_bin = prior_bin, prior_surv = prior_surv, prior_bin = prior_bin, bin_method = bin_method, binary_imputation = binary_imputation, bin_method = bin_method, binary_imputation = binary_imputation, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, block = block, rand_ratio = rand_ratio, prop_loss = prop_loss, alternative = alternative, h0 = h0, Fn = Fn, alternative = alternative, h0 = h0, Fn = Fn, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, Sn = Sn, Qn = Qn, prob_ha = prob_ha, N_impute = N_impute, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, N_mcmc = N_mcmc, mc_conf_level = mc_conf_level, method = method, imputed_final = imputed_final, method = method, imputed_final = imputed_final, empty_interval = empty_interval, return_trace = return_trace, empty_interval = empty_interval, return_trace = return_trace, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, prior_surv_final = prior_surv_final, generation_cutpoints = generation_cutpoints, rmst_tau = rmst_tau) rmst_tau = rmst_tau) attr(result, "arguments") <- NULL attr(result, "arguments") <- NULL if (inherits(result, "goldilocks_trial")) { if (inherits(result, "goldilocks_trial")) { attr(result$summary, "arguments") <- NULL attr(result$summary, "arguments") <- NULL } } list(trial = as.integer(x), result = result) list(trial = as.integer(x), result = result) }, error = function(error) { }, error = function(error) { list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], list(trial = as.integer(x), result = NULL, error_class = class(error)[1L], message = conditionMessage(error)) message = conditionMessage(error)) }) }) } } trial_index <- seq_len(N_trials) trial_index <- seq_len(N_trials) trial_results <- switch(backend, sequential = lapply(trial_index, trial_results <- switch(backend, sequential = lapply(trial_index, survival_adapt_wrapper), fork = pbmclapply(trial_index, survival_adapt_wrapper), fork = pbmclapply(trial_index, survival_adapt_wrapper, mc.cores = workers), psock = { survival_adapt_wrapper, mc.cores = workers), psock = { active_cluster <- make_sim_cluster(workers) active_cluster <- make_sim_cluster(workers) on.exit(stop_sim_cluster(active_cluster), add = TRUE) on.exit(stop_sim_cluster(active_cluster), add = TRUE) initialize_sim_cluster(active_cluster) initialize_sim_cluster(active_cluster) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) run_sim_cluster(active_cluster, trial_index, survival_adapt_wrapper) }) }) failed <- vapply(trial_results, function(x) is.null(x$result), failed <- vapply(trial_results, function(x) is.null(x$result), logical(1)) logical(1)) failed_results <- trial_results[failed] failed_results <- trial_results[failed] failures <- data.frame(trial = vapply(failed_results, `[[`, failures <- data.frame(trial = vapply(failed_results, `[[`, integer(1), "trial"), error_class = vapply(failed_results, integer(1), "trial"), error_class = vapply(failed_results, `[[`, character(1), "error_class"), message = vapply(failed_results, `[[`, character(1), "error_class"), message = vapply(failed_results, `[[`, character(1), "message"), stringsAsFactors = FALSE) `[[`, character(1), "message"), stringsAsFactors = FALSE) if (all(failed)) { if (all(failed)) { error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", error <- simpleError(paste0("All ", N_trials, " simulated trials failed. First error: ", failures$message[1L])) failures$message[1L])) error$failures <- failures error$failures <- failures class(error) <- c("goldilocks_all_trials_failed", class(error)) class(error) <- c("goldilocks_all_trials_failed", class(error)) stop(error) stop(error) } } successful_trials <- trial_results[!failed] successful_trials <- trial_results[!failed] successful_results <- lapply(successful_trials, `[[`, "result") successful_results <- lapply(successful_trials, `[[`, "result") if (return_trace) { if (return_trace) { sims <- bind_rows(lapply(successful_results, function(x) x$summary)) sims <- bind_rows(lapply(successful_results, function(x) x$summary)) traces <- bind_rows(lapply(seq_along(successful_results), traces <- bind_rows(lapply(seq_along(successful_results), function(i) { function(i) { trace <- successful_results[[i]]$trace trace <- successful_results[[i]]$trace trial <- successful_trials[[i]]$trial trial <- successful_trials[[i]]$trial trace$trial <- rep.int(trial, nrow(trace)) trace$trial <- rep.int(trial, nrow(trace)) trace[c("trial", setdiff(names(trace), "trial"))] trace[c("trial", setdiff(names(trace), "trial"))] })) })) out <- list(sims = sims, traces = traces, failures = failures, out <- list(sims = sims, traces = traces, failures = failures, call = Call) call = Call) } } else { else { sims <- bind_rows(successful_results) sims <- bind_rows(successful_results) out <- list(sims = sims, failures = failures, call = Call) out <- list(sims = sims, failures = failures, call = Call) } } attr(out$sims, "arguments") <- NULL attr(out$sims, "arguments") <- NULL attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, attr(out, "enrollment_design") <- new_enrollment_design(lambda = lambda, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, N_total = N_total, lambda_time = lambda_time, interim_look = interim_look, end_of_study = end_of_study) end_of_study = end_of_study) attr(out, "decision_design") <- attr(successful_results[[1]], attr(out, "decision_design") <- attr(successful_results[[1]], "decision_design", exact = TRUE) "decision_design", exact = TRUE) attr(out, "prior_design") <- attr(successful_results[[1]], attr(out, "prior_design") <- attr(successful_results[[1]], "prior_design", exact = TRUE) "prior_design", exact = TRUE) attr(out, "rng_metadata") <- rng_metadata attr(out, "rng_metadata") <- rng_metadata attr(out, "arguments") <- Arguments attr(out, "arguments") <- Arguments attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, attr(out, "parallel_metadata") <- list(requested_backend = requested_backend, backend = backend, selection_reason = execution$reason, backend = backend, selection_reason = execution$reason, requested_ncores = as.integer(ncores), workers = as.integer(workers), requested_ncores = as.integer(ncores), workers = as.integer(workers), tasks = as.integer(N_trials)) tasks = as.integer(N_trials)) if (nrow(failures) > 0L) { if (nrow(failures) > 0L) { warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", warning(nrow(failures), " of ", N_trials, " simulated trials failed and were excluded. See `result$failures` ", "for details.", call. = FALSE) "for details.", call. = FALSE) } } return(out) return(out)})(cutpoints = c(26, 52), generation_cutpoints = 52, N_total = 1000, })(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, 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, 8.66666666666667, 13, 17.3333333333333, 21.6666666666667, 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, 26), interim_look = c(400, 500, 600, 700, 800, 900), end_of_study = 69.3333333333333, prior_surv = c(1, 144.927536231884, 1, 144.927536231884, prior_surv = c(1, 144.927536231884, 1, 144.927536231884, 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, 1, 285.714285714286), block = 3, rand_ratio = c(control = 1, treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, treatment = 2), prop_loss = 0.1, alternative = "less", h0 = 0, Fn = c(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), Sn = c(1, 0.95, 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, 0.95, 0.95, 0.95, 0.95), prob_ha = 0.981, N_impute = 300, mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", mc_conf_level = 0.95, empty_interval = "prior", method = "logrank", imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 imputed_final = FALSE, hazard_treatment = c(0.005796, 0.00168 ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, ), hazard_control = c(0.00828, 0.0024), N_trials = 20, ncores = 2, seed = 3425430) seed = 3425430)
23: 23: do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, do.call(sim_trials, c(anthem_common, list(hazard_treatment = hazard_treatment_target_week, hazard_control = hazard_control_week, N_trials = 20, ncores = 2, hazard_control = hazard_control_week, N_trials = 20, ncores = 2, seed = 3425430))) seed = 3425430)))
24: 24: eval(expr, envir)eval(expr, envir)
25: 25: eval(expr, envir)eval(expr, envir)
26: 26: withVisible(eval(expr, envir))withVisible(eval(expr, envir))
27: 27: withCallingHandlers(code, error = function (e) withCallingHandlers(code, error = function (e) rlang::entrace(e), message = function (cnd) rlang::entrace(e), message = function (cnd) {{ watcher$capture_plot_and_output() watcher$capture_plot_and_output() if (on_message$capture) { if (on_message$capture) { watcher$push(cnd) watcher$push(cnd) } } if (on_message$silence) { if (on_message$silence) { invokeRestart("muffleMessage") invokeRestart("muffleMessage") } }}, warning = function (cnd) }, warning = function (cnd) {{ if (getOption("warn") >= 2 || getOption("warn") < 0) { if (getOption("warn") >= 2 || getOption("warn") < 0) { return() return() } } watcher$capture_plot_and_output() watcher$capture_plot_and_output() if (on_warning$capture) { if (on_warning$capture) { cnd <- sanitize_call(cnd) cnd <- sanitize_call(cnd) watcher$push(cnd) watcher$push(cnd) } } if (on_warning$silence) { if (on_warning$silence) { invokeRestart("muffleWarning") invokeRestart("muffleWarning") } }}, error = function (cnd) }, error = function (cnd) {{ watcher$capture_plot_and_output() watcher$capture_plot_and_output() cnd <- sanitize_call(cnd) watcher$push(cnd) cnd <- sanitize_call(cnd) switch(on_error, continue = invokeRestart("eval_continue"), watcher$push(cnd) stop = invokeRestart("eval_stop"), error = NULL) switch(on_error, continue = invokeRestart("eval_continue"), }) stop = invokeRestart("eval_stop"), error = NULL)
})28:
eval(call)28:
eval(call)29:
eval(call)29:
eval(call)30:
with_handlers({30: for (expr in tle$exprs) {with_handlers({ ev <- withVisible(eval(expr, envir)) for (expr in tle$exprs) { watcher$capture_plot_and_output() ev <- withVisible(eval(expr, envir)) watcher$print_value(ev$value, ev$visible, envir) watcher$capture_plot_and_output() } watcher$print_value(ev$value, ev$visible, envir) TRUE }}, handlers) TRUE
}, handlers)31:
doWithOneRestart(return(expr), restart)31:
doWithOneRestart(return(expr), restart)32:
withOneRestart(expr, restarts[[1L]])32:
withOneRestart(expr, restarts[[1L]])33:
withRestartList(expr, restarts[-nr])33:
withRestartList(expr, restarts[-nr])34:
doWithOneRestart(return(expr), restart)34:
doWithOneRestart(return(expr), restart)35:
withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])35:
withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])36:
withRestartList(expr, restarts)36:
withRestartList(expr, restarts)37:
withRestarts(with_handlers({37: for (expr in tle$exprs) {withRestarts(with_handlers({ ev <- withVisible(eval(expr, envir)) for (expr in tle$exprs) { watcher$capture_plot_and_output() ev <- withVisible(eval(expr, envir)) watcher$print_value(ev$value, ev$visible, envir) watcher$capture_plot_and_output() } watcher$print_value(ev$value, ev$visible, envir) TRUE }}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE) TRUE
}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE)38:
evaluate::evaluate(...)38:
evaluate::evaluate(...)39:
evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, 39: keep_message = if (is.numeric(options$message)) TRUE else options$message, evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, stop_on_error = if (is.numeric(options$error)) options$error else { keep_message = if (is.numeric(options$message)) TRUE else options$message, if (options$error && options$include) stop_on_error = if (is.numeric(options$error)) options$error else { 0L if (options$error && options$include) else 2L 0L }, output_handler = knit_handlers(options$render, options)) else 2L
}, output_handler = knit_handlers(options$render, options))40:
in_dir(input_dir(), expr)40:
in_dir(input_dir(), expr)41:
in_input_dir(evaluate(code, envir = env, new_device = FALSE, 41: keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, in_input_dir(evaluate(code, envir = env, new_device = FALSE, keep_message = if (is.numeric(options$message)) TRUE else options$message, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, stop_on_error = if (is.numeric(options$error)) options$error else { keep_message = if (is.numeric(options$message)) TRUE else options$message, if (options$error && options$include) stop_on_error = if (is.numeric(options$error)) options$error else { 0L if (options$error && options$include) else 2L 0L }, output_handler = knit_handlers(options$render, options))) else 2L
}, output_handler = knit_handlers(options$render, options)))42:
eng_r(options)42:
eng_r(options)43:
block_exec(params)43:
block_exec(params)44:
call_block(x)44:
call_block(x)45:
process_group(group)45:
process_group(group)46:
withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 46: error = function(e) {withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), if (progress && is.function(pb$interrupt)) error = function(e) { pb$interrupt() if (progress && is.function(pb$interrupt)) if (is_R_CMD_build() || is_R_CMD_check()) pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) }) error <<- format(e) })
47: 47: with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) {with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), if (progress && is.function(pb$interrupt)) error = function(e) { if (progress && is.function(pb$interrupt)) pb$interrupt() pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) error <<- format(e) }), list(rlang_trace_top_env = knit_global())) }), list(rlang_trace_top_env = knit_global()))
48: 48: xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), xfun:::handle_error(with_options(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) { error = function(e) { if (progress && is.function(pb$interrupt)) if (progress && is.function(pb$interrupt)) pb$interrupt() pb$interrupt() if (is_R_CMD_build() || is_R_CMD_check()) if (is_R_CMD_build() || is_R_CMD_check()) error <<- format(e) error <<- format(e) }), list(rlang_trace_top_env = knit_global())), function(loc) { }), list(rlang_trace_top_env = knit_global())), function(loc) { setwd(wd) setwd(wd) write_utf8(res, output %n% stdout()) write_utf8(res, output %n% stdout()) paste0("\nQuitting from ", loc, if (!is.null(error)) paste0("\nQuitting from ", loc, if (!is.null(error)) paste0("\n", rule(), error, "\n", rule())) paste0("\n", rule(), error, "\n", rule()))}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)}, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
49: 49: process_file(text, output)process_file(text, output)
50: 50: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
51: 51: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output_dir = getwd(), ...) output_dir = getwd(), ...)
52: 52: vweave_rmarkdown(...)vweave_rmarkdown(...)
53: 53: engine$weave(file, quiet = quiet, encoding = enc)engine$weave(file, quiet = quiet, encoding = enc)
54: 54: doTryCatch(return(expr), name, parentenv, handler)doTryCatch(return(expr), name, parentenv, handler)
55: 55: tryCatchOne(expr, names, parentenv, handlers[[1L]])tryCatchOne(expr, names, parentenv, handlers[[1L]])
56: 56: tryCatchList(expr, classes, parentenv, handlers)tryCatchList(expr, classes, parentenv, handlers)
57: 57: tryCatch({tryCatch({ engine$weave(file, quiet = quiet, encoding = enc) engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", output <- find_vignette_product(name, by = "texi2pdf", engine = engine) engine = engine) } }}, error = function(e) {}, error = function(e) { OK <<- FALSE OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e))) file, conditionMessage(e)))})})
58: 58: tools:::.buildOneVignette("anthem-hfref.Rmd", "/Volumes/Builds/packages/big-sur-arm64/results/4.5/goldilocks.Rcheck/vign_test/goldilocks", 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/RtmpFUQquN/file6e0927e026af.rds") TRUE, TRUE, "anthem-hfref", "UTF-8", "/Volumes/Temp/tmp/RtmpFUQquN/file6e0927e026af.rds")
An irrecoverable exception occurred. R is aborting now ...
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
2026-10-06 12:53:26.392 R[32899:347760] XType: Using static font registry.
--- finished re-building ‘calibrating-prob-ha.Rmd’
--- re-building ‘decision-traces.Rmd’ using rmarkdown
2026-10-06 12:53:28.204 R[32957:347924] 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
2026-10-06 12:53:39.686 R[33461:349409] XType: Using static font registry.
--- finished re-building ‘thermocool-af.Rmd’
--- re-building ‘two-arm.Rmd’ using rmarkdown
2026-10-06 12:53:41.516 R[33593:349804] 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