R Under development (unstable) (2024-11-22 r87365 ucrt) -- "Unsuffered Consequences"
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> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
> 
> library(testthat)
> library(BayesianFitForecast)
> 
> test_check("BayesianFitForecast")

> rm(list = ls())

> library(readxl)

> library(rstan)
Loading required package: StanHeaders

rstan version 2.32.6 (Stan version 2.32.2)

For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores()).
To avoid recompilation of unchanged Stan programs, we recommend calling
rstan_options(auto_write = TRUE)
For within-chain threading using `reduce_sum()` or `map_rect()` Stan functions,
change `threads_per_chain` option:
rstan_options(threads_per_chain = 1)

Do not specify '-march=native' in 'LOCAL_CPPFLAGS' or a Makevars file

> source("D:/RCompile/CRANincoming/R-devel/lib/BayesianFitForecast/extdata/option.R")

> source("D:/RCompile/CRANincoming/R-devel/lib/BayesianFitForecast/scripts/diff.R")

> source("D:/RCompile/CRANincoming/R-devel/lib/BayesianFitForecast/scripts/ode_rhs.R")

> source("D:/RCompile/CRANincoming/R-devel/lib/BayesianFitForecast/scripts/stancreator.R")

> stan_file <- "ode_model.stan"

> stan_code <- generate_stan_file()
Removed file: ode_model.stan 

> writeLines(stan_code, stan_file)

> Mydata <- read_excel(paste0(cadfilename1, ".xlsx"))

> for (i in 1:length(fitting_index)) {
+     assign(paste0("actualcases", i), Mydata[[paste0("cases", 
+         i)]])
+ }

> model <- stan_model("ode_model.stan")

> stan_code <- "ode_model.stan"

> for (calibrationperiod in calibrationperiods) {
+     for (i in 1:length(fitting_index)) {
+         assign(paste0("cases", i), as.integer(get(paste .... [TRUNCATED] 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 0.000323 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Iteration:   1 / 1000 [  0%]  (Warmup)
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Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 1: 
Chain 1:  Elapsed Time: 0.587 seconds (Warm-up)
Chain 1:                0.541 seconds (Sampling)
Chain 1:                1.128 seconds (Total)
Chain 1: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 0.000239 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 2.39 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2: 
Chain 2: 
Chain 2: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 2: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 2: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 2: Iteration: 300 / 1000 [ 30%]  (Warmup)
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Chain 2: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 2: Iteration: 501 / 1000 [ 50%]  (Sampling)
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Chain 2: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 2: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 2: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 2: 
Chain 2:  Elapsed Time: 11.369 seconds (Warm-up)
Chain 2:                0.496 seconds (Sampling)
Chain 2:                11.865 seconds (Total)
Chain 2: 

> rm(list = ls())

> library(bayesplot)
This is bayesplot version 1.11.1
- Online documentation and vignettes at mc-stan.org/bayesplot
- bayesplot theme set to bayesplot::theme_default()
   * Does _not_ affect other ggplot2 plots
   * See ?bayesplot_theme_set for details on theme setting

> library("readxl")

> library(xlsx)

> library(readxl)

> library(openxlsx)

Attaching package: 'openxlsx'

The following objects are masked from 'package:xlsx':

    createWorkbook, loadWorkbook, read.xlsx, saveWorkbook, write.xlsx


> library(rstan)

> library(ggplot2)

> source("D:/RCompile/CRANincoming/R-devel/lib/BayesianFitForecast/scripts/Metric_functions.R")

> source("D:/RCompile/CRANincoming/R-devel/lib/BayesianFitForecast/extdata/option.R")

> Mydata <- read_excel(paste0(cadfilename1, ".xlsx"))

> dir.create("output", showWarnings = FALSE)

> errorstructure <- c("negativebinomial", "normal", 
+     "poisson")

> for (i in 1:length(fitting_index)) {
+     assign(paste0("actualcases", i), Mydata[[paste0("cases", 
+         i)]])
+ }

> result_data1 <- data.frame()

> result_data2 <- data.frame()

> for (calibrationperiod in calibrationperiods) {
+     load(paste(model_name, "cal", (calibrationperiod), "fcst", 
+         forecastinghorizon, erro .... [TRUNCATED] 
[ FAIL 0 | WARN 1 | SKIP 0 | PASS 5 ]

[ FAIL 0 | WARN 1 | SKIP 0 | PASS 5 ]
> 
> proc.time()
   user  system elapsed 
  54.81    0.82  111.31