* using log directory 'd:/Rcompile/CRANpkg/local/4.5/miniLNM.Rcheck'
* using R version 4.5.3 (2026-03-11 ucrt)
* using platform: x86_64-w64-mingw32
* R was compiled by
    gcc.exe (GCC) 14.3.0
    GNU Fortran (GCC) 14.3.0
* running under: Windows Server 2022 x64 (build 20348)
* using session charset: UTF-8
* checking for file 'miniLNM/DESCRIPTION' ... OK
* this is package 'miniLNM' version '0.1.0'
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'miniLNM' can be installed ... OK
* used C++ compiler: 'g++.exe (GCC) 14.3.0'
* checking C++ specification ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking 'build' directory ... OK
* checking DESCRIPTION meta-information ... NOTE
  Missing dependency on R >= 4.1.0 because package code uses the pipe
  |> or function shorthand \(...) syntax added in R 4.1.0.
  File(s) using such syntax:
    'estimate.R' 'formula.R' 'toy_data.R'
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... [4s] OK
* checking whether the package can be loaded with stated dependencies ... [4s] OK
* checking whether the package can be unloaded cleanly ... [5s] OK
* checking whether the namespace can be loaded with stated dependencies ... [4s] OK
* checking whether the namespace can be unloaded cleanly ... [5s] OK
* checking loading without being on the library search path ... [9s] OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [11s] OK
* checking Rd files ... [1s] OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking line endings in shell scripts ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking compilation flags in Makevars ... OK
* checking for GNU extensions in Makefiles ... INFO
GNU make is a SystemRequirements.
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking use of PKG_*FLAGS in Makefiles ... OK
* checking pragmas in C/C++ headers and code ... OK
* checking compiled code ... OK
* checking installed files from 'inst/doc' ... OK
* checking files in 'vignettes' ... OK
* checking examples ... [11s] ERROR
Running examples in 'miniLNM-Ex.R' failed
The error most likely occurred in:

> ### Name: beta_mean
> ### Title: LNM Posterior Mean
> ### Aliases: beta_mean
> 
> ### ** Examples
> 
> example_data <- lnm_data(N = 50, K = 10)
> xy <- dplyr::bind_cols(example_data[c("X", "y")])
> fit <- lnm(
+     starts_with("y") ~ starts_with("x"), xy, 
+     iter = 25, output_samples = 25
+ )
Chain 1: ------------------------------------------------------------
Chain 1: EXPERIMENTAL ALGORITHM:
Chain 1:   This procedure has not been thoroughly tested and may be unstable
Chain 1:   or buggy. The interface is subject to change.
Chain 1: ------------------------------------------------------------
Chain 1: 
Chain 1: 
Chain 1: 
Chain 1: Gradient evaluation took 0.000637 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 6.37 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Begin eta adaptation.
Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
Chain 1: Success! Found best value [eta = 1] earlier than expected.
Chain 1: 
Chain 1: Begin stochastic gradient ascent.
Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
Chain 1: This variational approximation is not guaranteed to be meaningful.
Chain 1: 
Chain 1: Drawing a sample of size 25 from the approximate posterior... 
Chain 1: COMPLETED.
Error in if (p$diagnostics$pareto_k > 1) { : 
  missing value where TRUE/FALSE needed
Calls: lnm ... new -> initialize -> initialize -> vb -> vb -> .local
Execution halted
* checking for unstated dependencies in 'tests' ... OK
* checking tests ... [14s] ERROR
  Running 'testthat.R' [14s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > # 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(miniLNM)
  > 
  > test_check("miniLNM")
  Chain 1: ------------------------------------------------------------
  Chain 1: EXPERIMENTAL ALGORITHM:
  Chain 1:   This procedure has not been thoroughly tested and may be unstable
  Chain 1:   or buggy. The interface is subject to change.
  Chain 1: ------------------------------------------------------------
  Chain 1: 
  Chain 1: 
  Chain 1: 
  Chain 1: Gradient evaluation took 0.000596 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 5.96 seconds.
  Chain 1: Adjust your expectations accordingly!
  Chain 1: 
  Chain 1: 
  Chain 1: Begin eta adaptation.
  Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
  Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
  Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
  Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
  Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
  Chain 1: Success! Found best value [eta = 1] earlier than expected.
  Chain 1: 
  Chain 1: Begin stochastic gradient ascent.
  Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
  Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
  Chain 1: This variational approximation is not guaranteed to be meaningful.
  Chain 1: 
  Chain 1: Drawing a sample of size 25 from the approximate posterior... 
  Chain 1: COMPLETED.
  Saving _problems/test-estimate-7.R
  Chain 1: ------------------------------------------------------------
  Chain 1: EXPERIMENTAL ALGORITHM:
  Chain 1:   This procedure has not been thoroughly tested and may be unstable
  Chain 1:   or buggy. The interface is subject to change.
  Chain 1: ------------------------------------------------------------
  Chain 1: 
  Chain 1: 
  Chain 1: 
  Chain 1: Gradient evaluation took 0.000749 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 7.49 seconds.
  Chain 1: Adjust your expectations accordingly!
  Chain 1: 
  Chain 1: 
  Chain 1: Begin eta adaptation.
  Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
  Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
  Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
  Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
  Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
  Chain 1: Success! Found best value [eta = 1] earlier than expected.
  Chain 1: 
  Chain 1: Begin stochastic gradient ascent.
  Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
  Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
  Chain 1: This variational approximation is not guaranteed to be meaningful.
  Chain 1: 
  Chain 1: Drawing a sample of size 25 from the approximate posterior... 
  Chain 1: COMPLETED.
  Saving _problems/test-predict-7.R
  Chain 1: ------------------------------------------------------------
  Chain 1: EXPERIMENTAL ALGORITHM:
  Chain 1:   This procedure has not been thoroughly tested and may be unstable
  Chain 1:   or buggy. The interface is subject to change.
  Chain 1: ------------------------------------------------------------
  Chain 1: 
  Chain 1: 
  Chain 1: 
  Chain 1: Gradient evaluation took 0.000589 seconds
  Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 5.89 seconds.
  Chain 1: Adjust your expectations accordingly!
  Chain 1: 
  Chain 1: 
  Chain 1: Begin eta adaptation.
  Chain 1: Iteration:   1 / 250 [  0%]  (Adaptation)
  Chain 1: Iteration:  50 / 250 [ 20%]  (Adaptation)
  Chain 1: Iteration: 100 / 250 [ 40%]  (Adaptation)
  Chain 1: Iteration: 150 / 250 [ 60%]  (Adaptation)
  Chain 1: Iteration: 200 / 250 [ 80%]  (Adaptation)
  Chain 1: Success! Found best value [eta = 1] earlier than expected.
  Chain 1: 
  Chain 1: Begin stochastic gradient ascent.
  Chain 1:   iter             ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
  Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged.
  Chain 1: This variational approximation is not guaranteed to be meaningful.
  Chain 1: 
  Chain 1: Drawing a sample of size 25 from the approximate posterior... 
  Chain 1: COMPLETED.
  Saving _problems/test-sample-7.R
  [ FAIL 3 | WARN 0 | SKIP 0 | PASS 0 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-estimate.R:4:1'): (code run outside of `test_that()`) ──────────
  Error in `if (p$diagnostics$pareto_k > 1) {     warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is disabled.", " Decreasing tol_rel_obj may help if variational algorithm has terminated prematurely.", " Otherwise consider using sampling instead.", call. = FALSE, immediate. = TRUE) } else if (p$diagnostics$pareto_k > 0.7) {     warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is unreliable.", " Increasing the number of draws or decreasing tol_rel_obj may help.", call. = FALSE, immediate. = TRUE) }`: missing value where TRUE/FALSE needed
  Backtrace:
      ▆
   1. └─miniLNM::lnm(...) at test-estimate.R:4:1
   2.   ├─methods::new(...)
   3.   │ ├─methods::initialize(value, ...)
   4.   │ └─methods::initialize(value, ...)
   5.   ├─rstan::vb(stanmodels$lnm, data_list, ...)
   6.   └─rstan::vb(stanmodels$lnm, data_list, ...)
   7.     └─rstan (local) .local(object, ...)
  ── Error ('test-predict.R:4:1'): (code run outside of `test_that()`) ───────────
  Error in `if (p$diagnostics$pareto_k > 1) {     warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is disabled.", " Decreasing tol_rel_obj may help if variational algorithm has terminated prematurely.", " Otherwise consider using sampling instead.", call. = FALSE, immediate. = TRUE) } else if (p$diagnostics$pareto_k > 0.7) {     warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is unreliable.", " Increasing the number of draws or decreasing tol_rel_obj may help.", call. = FALSE, immediate. = TRUE) }`: missing value where TRUE/FALSE needed
  Backtrace:
      ▆
   1. └─miniLNM::lnm(...) at test-predict.R:4:1
   2.   ├─methods::new(...)
   3.   │ ├─methods::initialize(value, ...)
   4.   │ └─methods::initialize(value, ...)
   5.   ├─rstan::vb(stanmodels$lnm, data_list, ...)
   6.   └─rstan::vb(stanmodels$lnm, data_list, ...)
   7.     └─rstan (local) .local(object, ...)
  ── Error ('test-sample.R:4:1'): (code run outside of `test_that()`) ────────────
  Error in `if (p$diagnostics$pareto_k > 1) {     warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is disabled.", " Decreasing tol_rel_obj may help if variational algorithm has terminated prematurely.", " Otherwise consider using sampling instead.", call. = FALSE, immediate. = TRUE) } else if (p$diagnostics$pareto_k > 0.7) {     warning("Pareto k diagnostic value is ", round(p$diagnostics$pareto_k, 2), ". Resampling is unreliable.", " Increasing the number of draws or decreasing tol_rel_obj may help.", call. = FALSE, immediate. = TRUE) }`: missing value where TRUE/FALSE needed
  Backtrace:
      ▆
   1. └─miniLNM::lnm(...) at test-sample.R:4:1
   2.   ├─methods::new(...)
   3.   │ ├─methods::initialize(value, ...)
   4.   │ └─methods::initialize(value, ...)
   5.   ├─rstan::vb(stanmodels$lnm, data_list, ...)
   6.   └─rstan::vb(stanmodels$lnm, data_list, ...)
   7.     └─rstan (local) .local(object, ...)
  
  [ FAIL 3 | WARN 0 | SKIP 0 | PASS 0 ]
  Error:
  ! Test failures.
  Execution halted
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking re-building of vignette outputs ... [47s] OK
* checking PDF version of manual ... [16s] OK
* checking HTML version of manual ... [2s] OK
* DONE
Status: 2 ERRORs, 1 NOTE