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###
### Running command:
###
###   /home/biocbuild/R/R/bin/R CMD check --install=check:benchdamic.install-out.txt --library=/home/biocbuild/R/R/site-library --no-vignettes --timings benchdamic_1.16.0.tar.gz
###
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* using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/benchdamic.Rcheck’
* using R version 4.5.0 (2025-04-11)
* using platform: aarch64-unknown-linux-gnu
* R was compiled by
    aarch64-unknown-linux-gnu-gcc (GCC) 14.2.0
    GNU Fortran (GCC) 14.2.0
* running under: openEuler 24.03 (LTS)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘benchdamic/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘benchdamic’ version ‘1.16.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Imports includes 32 non-default packages.
Importing from so many packages makes the package vulnerable to any of
them becoming unavailable.  Move as many as possible to Suggests and
use conditionally.
* 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 for sufficient/correct file permissions ... OK
* checking whether package ‘benchdamic’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* 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 ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking dependencies in R code ... NOTE
Namespace in Imports field not imported from: ‘microbiome’
  All declared Imports should be used.
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Non-topic package-anchored link(s) in Rd file 'norm_DESeq2.Rd':
  ‘[DESeq2]{estimateSizeFactors}’

See section 'Cross-references' in the 'Writing R Extensions' manual.
* 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 contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... ERROR
Running examples in ‘benchdamic-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: plotLogP
> ### Title: plotLogP
> ### Aliases: plotLogP
> 
> ### ** Examples
> 
> # Load some data
> data(ps_stool_16S)
> 
> # Generate the patterns for 10 mock comparison for an experiment
> # (N = 1000 is suggested)
> mocks <- createMocks(nsamples = phyloseq::nsamples(ps_stool_16S), N = 10)
> head(mocks)
            [,1]   [,2]   [,3]   [,4]   [,5]   [,6]   [,7]   [,8]   [,9]  
Comparison1 "grp2" "grp2" "grp1" "grp2" "grp1" "grp1" "grp2" "grp1" "grp1"
Comparison2 "grp2" "grp2" "grp2" "grp1" "grp2" "grp2" "grp1" "grp1" "grp2"
Comparison3 "grp1" "grp1" "grp1" "grp1" "grp1" "grp2" "grp2" "grp2" "grp1"
Comparison4 "grp2" "grp2" "grp1" "grp1" "grp1" "grp2" "grp1" "grp2" "grp1"
Comparison5 "grp2" "grp1" "grp1" "grp2" "grp1" "grp1" "grp2" "grp2" "grp1"
Comparison6 "grp1" "grp2" "grp1" "grp1" "grp1" "grp2" "grp2" "grp1" "grp2"
            [,10]  [,11]  [,12]  [,13]  [,14]  [,15]  [,16]  [,17]  [,18] 
Comparison1 "grp2" "grp2" "grp1" "grp1" "grp2" "grp1" "grp1" "grp1" "grp2"
Comparison2 "grp2" "grp1" "grp2" "grp1" "grp2" "grp2" "grp1" "grp1" "grp1"
Comparison3 "grp2" "grp1" "grp2" "grp1" "grp1" "grp2" "grp1" "grp1" "grp1"
Comparison4 "grp1" "grp1" "grp2" "grp2" "grp2" "grp1" "grp1" "grp1" "grp2"
Comparison5 "grp1" "grp2" "grp2" "grp2" "grp2" "grp1" "grp2" "grp2" "grp1"
Comparison6 "grp2" "grp2" "grp1" "grp2" "grp1" "grp1" "grp2" "grp2" "grp2"
            [,19]  [,20]  [,21]  [,22]  [,23]  [,24]  [,25]  [,26]  [,27] 
Comparison1 "grp2" "grp2" "grp2" "grp1" "grp2" "grp1" "grp2" "grp1" "grp1"
Comparison2 "grp1" "grp1" "grp2" "grp1" "grp1" "grp1" "grp2" "grp1" "grp2"
Comparison3 "grp1" "grp2" "grp1" "grp1" "grp2" "grp2" "grp2" "grp2" "grp2"
Comparison4 "grp2" "grp2" "grp2" "grp2" "grp2" "grp2" "grp2" "grp1" "grp1"
Comparison5 "grp1" "grp2" "grp1" "grp1" "grp2" "grp1" "grp2" "grp2" "grp1"
Comparison6 "grp2" "grp2" "grp2" "grp2" "grp2" "grp1" "grp1" "grp1" "grp2"
            [,28]  [,29]  [,30]  [,31]  [,32] 
Comparison1 "grp1" "grp2" "grp2" "grp1" "grp2"
Comparison2 "grp1" "grp2" "grp1" "grp2" "grp2"
Comparison3 "grp2" "grp1" "grp2" "grp2" "grp2"
Comparison4 "grp1" "grp1" "grp1" "grp2" "grp1"
Comparison5 "grp2" "grp1" "grp1" "grp1" "grp2"
Comparison6 "grp1" "grp1" "grp1" "grp1" "grp1"
> 
> # Add some normalization/scaling factors to the phyloseq object
> my_norm <- setNormalizations(fun = c("norm_edgeR", "norm_CSS"),
+     method = c("TMM", "CSS"))
> ps_stool_16S <- runNormalizations(normalization_list = my_norm,
+     object = ps_stool_16S)
      + Running now:norm_edgeR

        Parameters:method=TMM

NF.TMM column has been added.
      + Running now:norm_CSS

        Parameters:method=CSS

Default value being used.
NF.CSS column has been added.
> 
> # Initialize some limma based methods
> my_limma <- set_limma(design = ~ group, coef = 2,
+     norm = c("TMM", "CSS"))
Warning in set_limma(design = ~group, coef = 2, norm = c("TMM", "CSS")) :
  DA_limma
One or more elements into 'norm' are not native to edgeR.
> 
> # Run methods on mock datasets
> results <- runMocks(mocks = mocks, method_list = my_limma,
+     object = ps_stool_16S)
  - Comparison1

      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=CSS, weights=FALSE

Warning in DA_limma(object = new("phyloseq", otu_table = new("otu_table",  :
  DA_limma
CSS normalization is not a native edgeR normalization. Make sure you know what you are doing, otherwise choose between 'TMM', 'TMMwsp', 'RLE', 'upperquartile', 'posupperquartile', or 'none'.
Automatically converting NF.CSS scaling/size factors to normalization factors.
Differential abundance on CSS normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=TMM, weights=FALSE

Differential abundance on TMM normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
  - Comparison2

      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=CSS, weights=FALSE

Warning in DA_limma(object = new("phyloseq", otu_table = new("otu_table",  :
  DA_limma
CSS normalization is not a native edgeR normalization. Make sure you know what you are doing, otherwise choose between 'TMM', 'TMMwsp', 'RLE', 'upperquartile', 'posupperquartile', or 'none'.
Automatically converting NF.CSS scaling/size factors to normalization factors.
Differential abundance on CSS normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=TMM, weights=FALSE

Differential abundance on TMM normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
  - Comparison3

      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=CSS, weights=FALSE

Warning in DA_limma(object = new("phyloseq", otu_table = new("otu_table",  :
  DA_limma
CSS normalization is not a native edgeR normalization. Make sure you know what you are doing, otherwise choose between 'TMM', 'TMMwsp', 'RLE', 'upperquartile', 'posupperquartile', or 'none'.
Automatically converting NF.CSS scaling/size factors to normalization factors.
Differential abundance on CSS normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=TMM, weights=FALSE

Differential abundance on TMM normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
  - Comparison4

      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=CSS, weights=FALSE

Warning in DA_limma(object = new("phyloseq", otu_table = new("otu_table",  :
  DA_limma
CSS normalization is not a native edgeR normalization. Make sure you know what you are doing, otherwise choose between 'TMM', 'TMMwsp', 'RLE', 'upperquartile', 'posupperquartile', or 'none'.
Automatically converting NF.CSS scaling/size factors to normalization factors.
Differential abundance on CSS normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=TMM, weights=FALSE

Differential abundance on TMM normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
  - Comparison5

      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=CSS, weights=FALSE

Warning in DA_limma(object = new("phyloseq", otu_table = new("otu_table",  :
  DA_limma
CSS normalization is not a native edgeR normalization. Make sure you know what you are doing, otherwise choose between 'TMM', 'TMMwsp', 'RLE', 'upperquartile', 'posupperquartile', or 'none'.
Automatically converting NF.CSS scaling/size factors to normalization factors.
Differential abundance on CSS normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=TMM, weights=FALSE

Differential abundance on TMM normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
  - Comparison6

      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=CSS, weights=FALSE

Warning in DA_limma(object = new("phyloseq", otu_table = new("otu_table",  :
  DA_limma
CSS normalization is not a native edgeR normalization. Make sure you know what you are doing, otherwise choose between 'TMM', 'TMMwsp', 'RLE', 'upperquartile', 'posupperquartile', or 'none'.
Automatically converting NF.CSS scaling/size factors to normalization factors.
Differential abundance on CSS normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=TMM, weights=FALSE

Differential abundance on TMM normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
  - Comparison7

      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=CSS, weights=FALSE

Warning in DA_limma(object = new("phyloseq", otu_table = new("otu_table",  :
  DA_limma
CSS normalization is not a native edgeR normalization. Make sure you know what you are doing, otherwise choose between 'TMM', 'TMMwsp', 'RLE', 'upperquartile', 'posupperquartile', or 'none'.
Automatically converting NF.CSS scaling/size factors to normalization factors.
Differential abundance on CSS normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=TMM, weights=FALSE

Differential abundance on TMM normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
  - Comparison8

      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=CSS, weights=FALSE

Warning in DA_limma(object = new("phyloseq", otu_table = new("otu_table",  :
  DA_limma
CSS normalization is not a native edgeR normalization. Make sure you know what you are doing, otherwise choose between 'TMM', 'TMMwsp', 'RLE', 'upperquartile', 'posupperquartile', or 'none'.
Automatically converting NF.CSS scaling/size factors to normalization factors.
Differential abundance on CSS normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=TMM, weights=FALSE

Differential abundance on TMM normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
  - Comparison9

      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=CSS, weights=FALSE

Warning in DA_limma(object = new("phyloseq", otu_table = new("otu_table",  :
  DA_limma
CSS normalization is not a native edgeR normalization. Make sure you know what you are doing, otherwise choose between 'TMM', 'TMMwsp', 'RLE', 'upperquartile', 'posupperquartile', or 'none'.
Automatically converting NF.CSS scaling/size factors to normalization factors.
Differential abundance on CSS normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=TMM, weights=FALSE

Differential abundance on TMM normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
  - Comparison10

      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=CSS, weights=FALSE

Warning in DA_limma(object = new("phyloseq", otu_table = new("otu_table",  :
  DA_limma
CSS normalization is not a native edgeR normalization. Make sure you know what you are doing, otherwise choose between 'TMM', 'TMMwsp', 'RLE', 'upperquartile', 'posupperquartile', or 'none'.
Automatically converting NF.CSS scaling/size factors to normalization factors.
Differential abundance on CSS normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
      * Running now:DA_limma

        Parameters:assay_name=counts, pseudo_count=FALSE, design=~group, coef=2, norm=TMM, weights=FALSE

Differential abundance on TMM normalized data
Estimating Differential Abundance without weighting
Extracting results for groupgrp2 coefficient
> 
> # Prepare results for Type I Error Control
> TIEC_summary <- createTIEC(results)
1. Extracting statistics
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
Using Method as id variables
2. Counting p-values lower than some thresholds
3. Counting adjusted p-values lower than some thresholds
4. Computing KS statistics
5. Ordering quantiles
> 
> # Plot the results
> plotFPR(df_FPR = TIEC_summary$df_FPR)
Using Comparison, Method as id variables
Examples with CPU (user + system) or elapsed time > 5s
                   user system elapsed
plotKS            4.586  3.908  20.645
plotConcordance   7.067  0.078   8.040
createConcordance 6.687  0.080   6.781
DA_ALDEx2         6.076  0.223   6.313
areaCAT           5.632  0.108   5.753
plotFPR           4.109  0.091   7.118
fitModels         3.538  0.040   6.270
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 1 ERROR, 2 NOTEs
See
  ‘/home/biocbuild/bbs-3.22-bioc/meat/benchdamic.Rcheck/00check.log’
for details.

##############################################################################
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###
### Running command:
###
###   /home/biocbuild/R/R/bin/R CMD INSTALL benchdamic
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/R/R-4.5.0/site-library’
* installing *source* package ‘benchdamic’ ...
** this is package ‘benchdamic’ version ‘1.16.0’
** using staged installation
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
** testing if installed package can be loaded from final location
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
** testing if installed package keeps a record of temporary installation path
* DONE (benchdamic)

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(testthat)
> library(benchdamic)
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by 'spam'
> 
> test_check("benchdamic")
|------------(25%)----------(50%)----------(75%)----------|
|------------(25%)----------(50%)----------(75%)----------|
|------------(25%)----------(50%)----------(75%)----------|
|------------(25%)----------(50%)----------(75%)----------|
|------------(25%)----------(50%)----------(75%)----------|
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 334 ]
> 
> proc.time()
   user  system elapsed 
 95.263 134.246 402.012