problems in R 4.3.0

[2723-ship]

source("_drake.R")
drake::make(plan, targets = "stan_output", lock_envir = FALSE)
▶ target stan_output
recompiling to avoid crashing R session
starting worker pid=7620 on localhost:11988 at 13:30:47.134
starting worker pid=7633 on localhost:11988 at 13:30:47.234
starting worker pid=7646 on localhost:11988 at 13:30:47.334
starting worker pid=7659 on localhost:11988 at 13:30:47.434

SAMPLING FOR MODEL '776747e20548c5cc12105b46e5d596ab' NOW (CHAIN 1).

SAMPLING FOR MODEL '776747e20548c5cc12105b46e5d596ab' NOW (CHAIN 2).

SAMPLING FOR MODEL '776747e20548c5cc12105b46e5d596ab' NOW (CHAIN 3).
✖ fail stan_output
Error: target stan_output failed.
diagnose(stan_output)$error$message:
error reading from connection
diagnose(stan_output)$error$calls:
run_eqn1_stan(data, list(phytoplankton = phyto_stan, zooplankton = zoo_stan))
plyr::dlply(data, .(sample), function(x, stan_list) {
stan_data <- list(N = nrow(x), Y = x$add15N_perc, Time_days = x$day,
NJ = length(unique(x$pond)), half_NJ = length(unique(x$pond))/2,
J = as.integer(as.factor(x$pond)))
type <- unique(x$sample)
model_code <- stan_list[[type]]
init_list <- list(create_new_inits(seed = 10, type), create_new_inits(seed = 23,
type), create_new_inits(seed = 31, type), create_new_inits(seed = 40,
type))
set.seed(15)
model <- rstan::stan(model_code = model_code, data = stan_data,
chains = 4, cores = 4, iter = 30000, warmup = 25000,
control = list(adapt_delta = 0.99, max_treedepth = 20),
init = init_list)
list(data = x, model = model)
}, ...)
llply(.

SAMPLING FOR MODEL '776747e20548c5cc12105b46e5d596ab' NOW (CHAIN 4).

✖ fail stan_output
Warning messages:
1: In mccollect(jobs) : 4 parallel jobs did not deliver results
2:
Having problems with parallel::mclapply(), future::future(), or furrr::future_map() in drake? Try one of the workarounds at https://books.ropensci.org/drake/hpc.html#parallel-computing-within-targets or ropensci/drake#675.

Error:
! in callr subprocess.
Caused by error:
! target stan_output failed.
diagnose(stan_output)$error$message:
no applicable method for @ applied to an object of class "NULL"

Having problems with parallel::mclapply(), future::future(), or furrr::future_map() in drake? Try one of the workarounds at https://books.ropensci.org/drake/hpc.html#parallel-computing-within-targets or ropensci/drake#675.

diagnose(stan_output)$error$calls:
run_eqn1_stan(data, list(phytoplankton = phyto_stan, zooplankton = zoo_stan))
plyr::dlply(data, .(sample), function(x, stan_list) {
stan_data <- list(N = nrow(x), Y = x$add15N_perc, Time_days = x$day,
NJ = length(unique(x$pond)), half_NJ = length(unique(x$pond))/2,
J = as.integer(as.factor(x$pond)))
type <- unique(x$sample)
model_code <- stan_list[[type]]
init_list <- list(create_new_inits(seed = 10, type), create_new_inits(seed = 23,
type), create_new_inits(seed = 31, type), create_new_inits(seed = 40,
type))
set.seed(15)
model <- rstan::stan(model_code = model_code, data = stan_data,
chains = 4, cores = 4, iter = 30000, warmup = 25000,
control = list(adapt_delta = 0.99, max_treedepth = 20),
init = init_list)
list(data = x, model = model)
}, ...)
llply(.data = pieces, .fun = .fun, ..., .progress = .progress,
.inform = .inform, .parallel = .parallel, .paropts = .paropts)
loop_apply(n, do.ply)
<fn>(1L)
.fun(piece, ...)
rstan::stan(model_code = model_code, data = stan_data, chains = 4,
cores = 4, iter = 30000, warmup = 25000, control = list(adapt_delta = 0.99,
max_treedepth = 20), init = init_list)
sampling(sm, data, pars, chains, iter, warmup, thin, seed, init,
check_data = TRUE, sample_file = sample_file, diagnostic_file = diagnostic_file,
verbose = verbose, algorithm = match.arg(algorithm), control = control,
check_unknown_args = FALSE, cores = cores, open_progress = open_progress,
include = include, ...)
sampling(sm, data, pars, chains, iter, warmup, thin, seed, init,
check_data = TRUE, sample_file = sample_file, diagnostic_file = diagnostic_file,
verbose = verbose, algorithm = match.arg(algorithm), control = control,
check_unknown_args = FALSE, cores = cores, open_progress = open_progress,
include = include, ...)
.local(object, ...)
sapply(nfits, FUN = function(x) x@mode == 0)
lapply(X = X, FUN = FUN, ...)
FUN(X[[i]], ...)
ℹ See $stdout and $stderr for standard output and error.
Type .Last.error to see the more details.