CRAN Package Check Results for Package fitdistrBayes

Last updated on 2026-09-27 05:51:40 CEST.

Flavor	Version	Tinstall	Tcheck	Ttotal	Status	Flags
r-devel-linux-x86_64-debian-clang	0.5.0	8.91	196.60	205.51	OK
r-devel-linux-x86_64-debian-gcc	0.5.0	6.25	121.96	128.21	OK
r-devel-linux-x86_64-fedora-clang	0.5.0			125.84	OK
r-devel-linux-x86_64-fedora-gcc	0.5.0			118.74	OK
r-devel-windows-x86_64	0.5.0	10.00	184.00	194.00	OK
r-patched-linux-x86_64	0.5.0	9.14	186.32	195.46	OK
r-release-linux-x86_64	0.5.0	8.58	183.07	191.65	OK
r-release-macos-arm64	0.5.0	2.00	16.00	18.00	ERROR
r-release-macos-x86_64	0.5.0	7.00	312.00	319.00	OK
r-release-windows-x86_64	0.2.3	12.00	195.00	207.00	OK
r-oldrel-macos-arm64	0.5.0	3.00	26.00	29.00	ERROR
r-oldrel-macos-x86_64	0.5.0	7.00	278.00	285.00	OK
r-oldrel-windows-x86_64	0.5.0	14.00	241.00	255.00	OK

Check Details

Version: 0.5.0
Check: tests
Result: ERROR Running ‘censored-helpers.R’ [0s/0s] Running ‘tests_audit_regressions.R’ [0s/0s] Running the tests in ‘tests/tests_audit_regressions.R’ failed. Complete output: > library(fitdistrBayes) > local({ + E <- asNamespace("fitdistrBayes") + must_error <- function(expr,pattern) { + msg <- tryCatch({ force(expr); "NO ERROR" },error=conditionMessage) + stopifnot(grepl(pattern,msg,ignore.case=TRUE)) + } + set.seed(100); state <- .Random.seed + must_error(fitdistrBayes(1:20,"exponential","reference",criterias=TRUE),"Unused arguments") + must_error(fitdistrBayes(array(1:8,c(2,2,2)),"exponential","reference"),"numeric vector") + must_error(fitcensBayes(1:3+0i,c(1,1,1),"exponential","reference"),"numeric vector") + stopifnot(identical(state,.Random.seed)) + # Stable inverse of the weighted Lindley mean over 400 orders of magnitude. + for (mu in c(1e-200,1e-160,1,1e160,1e200)) for (phi in c(.2,2,1e100)) { + lambda <- E$.fdb_weighted_lindley_lambda_from_mean(mu,phi) + stopifnot(is.finite(lambda),lambda>0) + log_mean <- log(phi) + E$.fdb_logsumexp(c(log(lambda),log(phi),0)) - + log(lambda) - E$.fdb_logsumexp(c(log(lambda),log(phi))) + stopifnot(abs(log_mean-log(mu))<1e-10) + } + tr <- E$.cens_transform("weighted lindley",c(lambda=1e200,phi=2)) + u <- tr$to(c(lambda=1e200,phi=2)) + stopifnot(all(is.finite(u)),max(abs(log(tr$from(u)$theta/c(lambda=1e200,phi=2))))<1e-10) + tr <- E$.cens_transform("beta",c(shape1=1,shape2=1)) + for (u in list(c(40,40),c(-40,40),c(750,750),c(-750,750))) { + z <- tr$from(u) + if (abs(u[1])==40) stopifnot(all(is.finite(z$theta)),all(z$theta>0),is.finite(z$jacobian)) + } + stopifnot(abs(E$.cens_transform("gamma",c(shape=1e200,rate=1e-200))$to(c(shape=1e200,rate=1e-200))[1]-400*log(10))<1e-10) + + # Scale-invariant mean ESS and representable standard deviations/SEs. + set.seed(200) + mat <- replicate(4,as.numeric(arima.sim(list(ar=.95),n=1000))) + base <- E$.fdb_ess_matrix(mat) + stopifnot(base<1000) + for (factor in c(1e-200,1e-10,1e200)) { + stopifnot(abs(E$.fdb_ess_matrix(mat*factor)/base-1)<1e-10, + abs(E$.fdb_stable_sd(as.numeric(mat)*factor)/(sd(as.numeric(mat))*factor)-1)<1e-10) + } + stopifnot(abs(E$.fdb_ess_matrix(mat+1e6)/base-1)<1e-5, + abs(E$.fdb_ic_se(c(1e200,2e200,3e200))/(sqrt(3)*1e200)-1)<1e-12, + abs(E$.fdb_ic_se(c(1e-200,2e-200,3e-200))/(sqrt(3)*1e-200)-1)<1e-12) + frozen <- E$.fdb_summarize(list(matrix(1,40,1,dimnames=list(NULL,"p")), + matrix(1,40,1,dimnames=list(NULL,"p"))),FALSE) + stopifnot(is.infinite(frozen$rhat),frozen$ess_bulk==0,frozen$ess_mean==0, + frozen$ess_tail==0,is.na(frozen$mcse_mean)) + + a <- fitdistrBayes(1:20,"exponential","reference",iter=500,warmup=250,chains=2,seed=9) + big <- a; big$.loglik <- function(theta) rep(-1e307,20) + result <- suppressWarnings(WAIC(big)) + stopifnot(!result$estimates$available,!result$estimates$reliable,is.na(result$estimates$estimate), + nzchar(result$estimates$reason)) + broken <- a; broken$.loglik <- function(theta) rep(NaN,20) + result <- suppressWarnings(E$.fdb_criteria_after_fit(broken,c("waic","dic"))) + stopifnot(inherits(result,"fitdistrBayes_criteria"),!any(result$estimates$available), + length(result$diagnostics$computation_error)==1L,identical(broken$chains,a$chains)) + + spec <- fitdistrBayes_model( + density=function(x,theta,log=FALSE) { + v <- if(abs(theta)<.5) rep(-Inf,length(x)) else dexp(x,1,log=TRUE) + if(log) v else exp(v) + }, + prior=function(theta,log=FALSE) { + v <- if(abs(theta)>=.5 && abs(theta)<=2) -log(3) else -Inf + if(log) v else exp(v) + },start=c(theta=1),lower=-2,upper=2,name="disconnected",engine="custom",independent=TRUE, + sampler=function(chains,n_save,...) lapply(seq_len(chains),function(i) + matrix(sample(c(-1,1),n_save,TRUE)*runif(n_save,.5,2),ncol=1,dimnames=list(NULL,"theta"))), + propriety=TRUE,moments=data.frame(parameter="theta",mean_exists=TRUE,variance_exists=TRUE)) + fit <- suppressWarnings(fitdistrBayes(c(.5,1,2),spec,iter=200,warmup=100,chains=2,seed=3, + criteria=c("waic","dic"))) + stopifnot(inherits(fit,"fitdistrBayes"),identical(fit$criteria$estimates$available,c(TRUE,FALSE))) + + # A sure censoring event has an exactly zero contribution, not a bad Pareto tail. + if(requireNamespace("loo",quietly=TRUE)) { + b <- fitcensBayes(c(1:20,0),c(rep(1,20),0),"exponential","reference", + iter=500,warmup=250,chains=2,seed=9) + messages <- character() + cb <- withCallingHandlers(LOOIC(b),warning=function(w) { + messages <<- c(messages,conditionMessage(w)); invokeRestart("muffleWarning") + }) + ca <- LOOIC(a) + stopifnot(!length(messages),cb$estimates$reliable, + abs(cb$estimates$estimate-ca$estimates$estimate)<1e-10, + identical(cb$details$loo_zero_information_observations,21L), + all(log_lik(b)[,21]==0), + cb$details$looic$pointwise[21,"looic"]==0, + cb$details$looic$pointwise[21,"mcse_elpd_loo"]==0, + identical(dim(cb$details$looic),c(500L,21L))) + invisible(capture.output(print(cb$details$looic))) + invisible(loo::loo_compare(cb$details$looic,cb$details$looic)) + # Count models at zero are not sure censoring events and must not be removed. + discrete <- fitcensBayes(c(1,2,3,0),c(1,1,1,0),"Poisson","jeffreys", + iter=100,warmup=50,chains=2,seed=8,control=list(warn_convergence=FALSE)) + stopifnot(!length(E$.fdb_zero_information(discrete))) + } + cat("PASS: numerical extremes, unit-invariant ESS, frozen chains, optional failure isolation, and zero-information LOO.\n") + }) Error: WAIC arithmetic exceeded the numerical range. Execution halted Flavors: r-release-macos-arm64, r-oldrel-macos-arm64