R Under development (unstable) (2024-12-15 r87442 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(GrowthCurveME)
> 
> test_check("GrowthCurveME")
The number of subjects is small, increasing the number of chains to 5 to improve convergence
The number of subjects is small, increasing the number of chains to 5 to improve convergence
The number of subjects is small, increasing the number of chains to 10 to improve convergence
The number of subjects is small, increasing the number of chains to 10 to improve convergence
Error in solve.default(FO) : 
  system is computationally singular: reciprocal condition number = 1.6041e-18
The number of subjects is small, increasing the number of chains to 5 to improve convergence
Number of clusters: 10 
Number of unique time points: 24 
Number of observations: 240 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
The number of subjects is small, increasing the number of chains to 5 to improve convergence
Number of clusters: 10 
Number of unique time points: 11 
Number of observations: 110 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
The number of subjects is small, increasing the number of chains to 2 to improve convergence
Number of clusters: 25 
Number of unique time points: 32 
Number of observations: 800 
The number of subjects is small, increasing the number of chains to 2 to improve convergence
The number of subjects is small, increasing the number of chains to 10 to improve convergence
Number of clusters: 5 
Number of unique time points: 42 
Number of observations: 210 
The number of subjects is small, increasing the number of chains to 10 to improve convergence
Number of clusters: 10 
Number of unique time points: 24 
Number of observations: 240 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
Simulating data using nsim = 1000 simulated datasets
Computing WRES and npde .
Number of clusters: 10 
Number of unique time points: 24 
Number of observations: 240 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
Simulating data using nsim = 1000 simulated datasets
Computing WRES and npde .
Number of clusters: 10 
Number of unique time points: 24 
Number of observations: 240 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
Simulating data using nsim = 1000 simulated datasets
Computing WRES and npde .
Number of clusters: 10 
Number of unique time points: 24 
Number of observations: 240 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
Simulating data using nsim = 1000 simulated datasets
Computing WRES and npde .
Number of clusters: 10 
Number of unique time points: 24 
Number of observations: 240 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
Simulating data using nsim = 1000 simulated datasets
Computing WRES and npde .
Number of unique time points: 24 
Number of observations: 240 
Number of unique time points: 24 
Number of observations: 240 
Number of clusters: 10 
Number of unique time points: 24 
Number of observations: 240 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
Simulating data using nsim = 1000 simulated datasets
Computing WRES and npde .
Number of unique time points: 24 
Number of observations: 240 
Number of clusters: 10 
Number of unique time points: 24 
Number of observations: 240 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
Simulating data using nsim = 1000 simulated datasets
Computing WRES and npde .
Number of clusters: 10 
Number of unique time points: 24 
Number of observations: 240 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
Simulating data using nsim = 1000 simulated datasets
Computing WRES and npde .
Number of clusters: 10 
Number of unique time points: 24 
Number of observations: 240 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
Simulating data using nsim = 1000 simulated datasets
Computing WRES and npde .
Number of clusters: 10 
Number of unique time points: 24 
Number of observations: 240 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
Simulating data using nsim = 1000 simulated datasets
Computing WRES and npde .
Number of unique time points: 24 
Number of observations: 240 
Number of unique time points: 24 
Number of observations: 240 
Number of unique time points: 24 
Number of observations: 240 
Number of unique time points: 24 
Number of observations: 240 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
The number of subjects is small, increasing the number of chains to 5 to improve convergence
The number of subjects is small, increasing the number of chains to 2 to improve convergence
The number of subjects is small, increasing the number of chains to 2 to improve convergence
Error in solve.default(FO) : 
  system is computationally singular: reciprocal condition number = 1.29391e-17
Number of clusters: 10 
Number of unique time points: 24 
Number of observations: 240 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
Simulating data using nsim = 1000 simulated datasets
Computing WRES and npde .
Number of clusters: 10 
Number of unique time points: 24 
Number of observations: 240 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
Simulating data using nsim = 1000 simulated datasets
Computing WRES and npde .
Simulating data using nsim = 1000 simulated datasets
Computing WRES and npde .
Number of unique time points: 24 
Number of observations: 240 
Number of unique time points: 24 
Number of observations: 240 
Number of clusters: 10 
Number of unique time points: 24 
Number of observations: 240 
The number of subjects is small, increasing the number of chains to 5 to improve convergence
Simulating data using nsim = 1000 simulated datasets
Computing WRES and npde .
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 43 ]
> 
> proc.time()
   user  system elapsed 
 174.15   18.54  192.68