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### Running command:
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###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:DaMiRseq.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings DaMiRseq_2.8.0.tar.gz
###
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* using log directory ‘/Users/biocbuild/bbs-3.15-bioc/meat/DaMiRseq.Rcheck’
* using R version 4.2.1 (2022-06-23)
* using platform: x86_64-apple-darwin17.0 (64-bit)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘DaMiRseq/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘DaMiRseq’ version ‘2.8.0’
* 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 for sufficient/correct file permissions ... OK
* checking whether package ‘DaMiRseq’ 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 R 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 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 ... NOTE
DaMiR.Clustplot: warning in pheatmap(count_data,
  clustering_distance_rows = d_r, clustering_distance_cols = d_c, scale
  = "row", col = colors, annotation_col = df): partial argument match
  of 'col' to 'color'
DaMiR.Allplot: no visible binding for global variable ‘X1’
DaMiR.Allplot: no visible binding for global variable ‘X2’
DaMiR.Allplot: no visible binding for global variable ‘PC1’
DaMiR.Allplot: no visible binding for global variable ‘PC2’
DaMiR.Allplot: no visible binding for global variable ‘value’
DaMiR.Allplot: no visible binding for global variable ‘variable’
DaMiR.EnsembleLearning2cl: no visible binding for global variable
  ‘Classifiers’
DaMiR.EnsembleLearning2cl: no visible binding for global variable
  ‘Accuracy’
DaMiR.EnsembleLearning2cl: no visible binding for global variable ‘MCC’
DaMiR.EnsembleLearning2cl: no visible binding for global variable
  ‘Specificity’
DaMiR.EnsembleLearning2cl: no visible binding for global variable
  ‘Sensitivity’
DaMiR.EnsembleLearning2cl: no visible binding for global variable ‘PPV’
DaMiR.EnsembleLearning2cl: no visible binding for global variable ‘NPV’
DaMiR.EnsembleLearning2cl: no visible global function definition for
  ‘colSds’
DaMiR.EnsembleLearningNcl: no visible binding for global variable
  ‘Classifiers’
DaMiR.EnsembleLearningNcl: no visible binding for global variable
  ‘Accuracy’
DaMiR.EnsembleLearningNcl: no visible global function definition for
  ‘colSds’
DaMiR.MDSplot: no visible binding for global variable ‘X1’
DaMiR.MDSplot: no visible binding for global variable ‘X2’
DaMiR.ModelSelect: no visible binding for global variable ‘Metrics’
DaMiR.ModelSelect: no visible binding for global variable
  ‘N.predictors’
DaMiR.ModelSelect: no visible binding for global variable ‘Counts’
DaMiR.iTSadjust: no visible binding for global variable ‘value’
DaMiR.iTSadjust: no visible binding for global variable ‘variable’
DaMiR.iTSnorm: no visible binding for global variable ‘value’
DaMiR.iTSnorm: no visible binding for global variable ‘variable’
Undefined global functions or variables:
  Accuracy Classifiers Counts MCC Metrics N.predictors NPV PC1 PC2 PPV
  Sensitivity Specificity X1 X2 colSds value variable
* checking Rd files ... 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 contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking sizes of PDF files under ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
               user system elapsed
DaMiR.Allplot 8.463  0.265   8.735
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in ‘inst/doc’ ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 1 NOTE
See
  ‘/Users/biocbuild/bbs-3.15-bioc/meat/DaMiRseq.Rcheck/00check.log’
for details.

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###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL DaMiRseq
###
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* installing to library ‘/Library/Frameworks/R.framework/Versions/4.2/Resources/library’
* installing *source* package ‘DaMiRseq’ ...
** using staged installation
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (DaMiRseq)

R version 4.2.1 (2022-06-23) -- "Funny-Looking Kid"
Copyright (C) 2022 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin17.0 (64-bit)

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(DaMiRseq)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'MatrixGenerics'

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

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
    colWeightedMeans, colWeightedMedians, colWeightedSds,
    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars

Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

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

    IQR, mad, sd, var, xtabs

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

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
    tapply, union, unique, unsplit, which.max, which.min

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

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

    I, expand.grid, unname

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

The following object is masked from 'package:MatrixGenerics':

    rowMedians

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

    anyMissing, rowMedians

Loading required package: ggplot2
> 
> test_check("DaMiRseq")
10 Predictors have been selected for classification 
3 Predictors have been selected for classification 
100 Highly correlated features have been discarded for classification. 
 0 Features remained. 
100 Highly correlated features have been discarded for classification. 
 0 Features remained. 
Your dataset has: 
100 Features; 
11 Samples, divided in:  
3 variables: a b class ; 
 'class' included. 
Your dataset has: 
100 Features; 
11 Samples, divided in:  
3 variables: a b class ; 
 'class' included. 
0 Features have been filtered out by espression. 100 Features remained. 
0 'Hypervariant' Features have been filtered out. 100 Features remained. 
Performing Normalization by 'vst' with dispersion parameter:  parametric 
0 Samples have been excluded by averaged Sample-per-Sample correlation. 
 40 Samples remained. 
The number of SVs identified, which explain 95 % of Variance, is: 4 
All the sv have been used to adjust the dataAll the sv have been used to adjust the data[ FAIL 0 | WARN 0 | SKIP 0 | PASS 106 ]
> 
> proc.time()
   user  system elapsed 
 40.862   1.748  41.594