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### Running command:
###
###   /home/biocbuild/bbs-3.16-bioc/R/bin/R CMD check --install=check:DMCHMM.install-out.txt --library=/home/biocbuild/bbs-3.16-bioc/R/site-library --timings DMCHMM_1.20.0.tar.gz
###
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* using log directory ‘/home/biocbuild/bbs-3.16-bioc/meat/DMCHMM.Rcheck’
* using R version 4.2.3 (2023-03-15)
* using platform: x86_64-pc-linux-gnu (64-bit)
* using session charset: UTF-8
* checking for file ‘DMCHMM/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘DMCHMM’ version ‘1.20.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... NOTE
Depends: includes the non-default packages:
  'SummarizedExperiment', 'S4Vectors', 'BiocParallel', 'GenomicRanges',
  'IRanges', 'fdrtool'
Adding so many packages to the search path is excessive and importing
selectively is preferable.
* 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 ‘DMCHMM’ 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 loading without being on the library search path ... 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 ... OK
* 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 files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                       user system elapsed
manhattanDMCs-method 72.591 24.683  47.014
findDMCs-method      69.990 24.728  45.182
qqDMCs-method        68.686 23.523  43.780
methHMMCMC-method    49.461  5.495  27.522
methHMEM-method       3.207  1.828   2.529
* 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 ...
  ‘DMCHMM.Rmd’ using ‘UTF-8’... OK
 NONE
* checking re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE

Status: 1 NOTE
See
  ‘/home/biocbuild/bbs-3.16-bioc/meat/DMCHMM.Rcheck/00check.log’
for details.

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###
### Running command:
###
###   /home/biocbuild/bbs-3.16-bioc/R/bin/R CMD INSTALL DMCHMM
###
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* installing to library ‘/home/biocbuild/bbs-3.16-bioc/R/site-library’
* installing *source* package ‘DMCHMM’ ...
** using staged installation
** R
** 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 (DMCHMM)

R version 4.2.3 (2023-03-15) -- "Shortstop Beagle"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (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(DMCHMM)
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, aperm, 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: BiocParallel
Loading required package: fdrtool
DMCHMM package, Version 1.20.0, Released 2020-09-27
A pipeline for identifying differentially methylated CpG sites 
    using Hidden Markov Model in bisulfite sequencing data. DNA methylation 
    studies have enabled researchers to understand methylation patterns and 
    their regulatory roles in biological processes and disease. However, only 
    a limited number of statistical approaches have been developed to provide 
    formal quantitative analysis. Specifically, a few available methods do 
    identify differentially methylated CpG (DMC) sites or regions (DMR), but 
    they suffer from limitations that arise mostly due to challenges inherent 
    in bisulfite sequencing data. These challenges include: (1) that 
    read-depths vary considerably among genomic positions and are often low; 
    (2) both methylation and autocorrelation patterns change as regions change; 
    and (3) CpG sites are distributed unevenly. Furthermore, there are several 
    methodological limitations: almost none of these tools is capable of 
    comparing multiple groups and/or working with missing values, and only a 
    few allow continuous or multiple covariates. The last of these is of great 
    interest among researchers, as the goal is often to find which regions of 
    the genome are associated with several exposures and traits. To tackle 
    these issues, we have developed an efficient DMC identification method 
    based on Hidden Markov Models (HMMs) called “DMCHMM” which is a three-step 
    approach (model selection, prediction, testing) aiming to address the 
    aforementioned drawbacks.
BugReports: https://github.com/shokoohi/DMCHMM/issues

Attaching package: 'DMCHMM'

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

    combine

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

    combine

> 
> test_check("DMCHMM")

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[ FAIL 0 | WARN 0 | SKIP 0 | PASS 1 ]
> 
> proc.time()
   user  system elapsed 
 17.874   5.240  16.341