Back to Multiple platform build/check report for BioC 3.14
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This page was generated on 2022-01-05 13:07:50 -0500 (Wed, 05 Jan 2022).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 20.04.4 LTS)x86_644.1.2 (2021-11-01) -- "Bird Hippie" 4327
tokay2Windows Server 2012 R2 Standardx644.1.2 (2021-11-01) -- "Bird Hippie" 4076
machv2macOS 10.14.6 Mojavex86_644.1.2 (2021-11-01) -- "Bird Hippie" 4137
Click on any hostname to see more info about the system (e.g. compilers)      (*) as reported by 'uname -p', except on Windows and Mac OS X

CHECK results for DMCHMM on tokay2


To the developers/maintainers of the DMCHMM package:
- Please allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/DMCHMM.git to
reflect on this report. See How and When does the builder pull? When will my changes propagate? here for more information.
- Make sure to use the following settings in order to reproduce any error or warning you see on this page.

raw results

Package 524/2083HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
DMCHMM 1.16.0  (landing page)
Farhad Shokoohi
Snapshot Date: 2022-01-04 01:55:05 -0500 (Tue, 04 Jan 2022)
git_url: https://git.bioconductor.org/packages/DMCHMM
git_branch: RELEASE_3_14
git_last_commit: efaf39a
git_last_commit_date: 2021-10-26 12:37:33 -0500 (Tue, 26 Oct 2021)
nebbiolo2Linux (Ubuntu 20.04.4 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
tokay2Windows Server 2012 R2 Standard / x64  OK    OK    WARNINGS    OK  UNNEEDED, same version is already published
machv2macOS 10.14.6 Mojave / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published

Summary

Package: DMCHMM
Version: 1.16.0
Command: C:\Users\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:DMCHMM.install-out.txt --library=C:\Users\biocbuild\bbs-3.14-bioc\R\library --no-vignettes --timings DMCHMM_1.16.0.tar.gz
StartedAt: 2022-01-04 18:43:39 -0500 (Tue, 04 Jan 2022)
EndedAt: 2022-01-04 18:56:18 -0500 (Tue, 04 Jan 2022)
EllapsedTime: 759.1 seconds
RetCode: 0
Status:   WARNINGS  
CheckDir: DMCHMM.Rcheck
Warnings: 1

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\Users\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:DMCHMM.install-out.txt --library=C:\Users\biocbuild\bbs-3.14-bioc\R\library --no-vignettes --timings DMCHMM_1.16.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory 'C:/Users/biocbuild/bbs-3.14-bioc/meat/DMCHMM.Rcheck'
* using R version 4.1.2 (2021-11-01)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'DMCHMM/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'DMCHMM' version '1.16.0'
* 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 whether package 'DMCHMM' can be installed ... WARNING
Found the following significant warnings:
  Warning: replacing previous import 'IRanges::windows' by 'grDevices::windows' when loading 'DMCHMM'
See 'C:/Users/biocbuild/bbs-3.14-bioc/meat/DMCHMM.Rcheck/00install.out' for details.
* 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
* loading checks for arch 'i386'
** 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
* loading checks for arch 'x64'
** 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 ... 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 ...
** running examples for arch 'i386' ... OK
Examples with CPU (user + system) or elapsed time > 5s
                      user system elapsed
methHMMCMC-method    62.64   0.08   62.73
qqDMCs-method        62.44   0.24   62.69
manhattanDMCs-method 61.80   0.08   61.89
findDMCs-method      60.95   0.20   61.16
** running examples for arch 'x64' ... OK
Examples with CPU (user + system) or elapsed time > 5s
                      user system elapsed
findDMCs-method      64.53   0.11   64.64
manhattanDMCs-method 57.45   0.04   57.50
qqDMCs-method        53.98   0.07   54.05
methHMMCMC-method    52.97   0.11   53.08
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
** running tests for arch 'i386' ...
  Running 'testthat.R'
 OK
** running tests for arch 'x64' ...
  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 WARNING, 1 NOTE
See
  'C:/Users/biocbuild/bbs-3.14-bioc/meat/DMCHMM.Rcheck/00check.log'
for details.



Installation output

DMCHMM.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\cygwin\bin\curl.exe -O http://155.52.207.166/BBS/3.14/bioc/src/contrib/DMCHMM_1.16.0.tar.gz && rm -rf DMCHMM.buildbin-libdir && mkdir DMCHMM.buildbin-libdir && C:\Users\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=DMCHMM.buildbin-libdir DMCHMM_1.16.0.tar.gz && C:\Users\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD INSTALL DMCHMM_1.16.0.zip && rm DMCHMM_1.16.0.tar.gz DMCHMM_1.16.0.zip
###
##############################################################################
##############################################################################


  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100  756k  100  756k    0     0  3139k      0 --:--:-- --:--:-- --:--:-- 3153k

install for i386

* installing *source* package 'DMCHMM' ...
** using staged installation
** R
** inst
** byte-compile and prepare package for lazy loading
Warning: replacing previous import 'IRanges::windows' by 'grDevices::windows' when loading 'DMCHMM'
** help
*** installing help indices
  converting help for package 'DMCHMM'
    finding HTML links ... done
    BSDMCs-class                            html  
    BSData-class                            html  
    finding level-2 HTML links ... done

    DMCHMM-package                          html  
    cBSDMCs-method                          html  
    cBSData-method                          html  
    combine-method                          html  
    data                                    html  
    findDMCs-method                         html  
    manhattanDMCs-method                    html  
    methHMEM-method                         html  
    methHMMCMC-method                       html  
    methLevels-method                       html  
    methReads-method                        html  
    methStates-method                       html  
    methVars-method                         html  
    params                                  html  
    qqDMCs-method                           html  
    readBismark-method                      html  
    totalReads-method                       html  
    writeBED-method                         html  
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
Warning: replacing previous import 'IRanges::windows' by 'grDevices::windows' when loading 'DMCHMM'
** testing if installed package can be loaded from final location
Warning: replacing previous import 'IRanges::windows' by 'grDevices::windows' when loading 'DMCHMM'
** testing if installed package keeps a record of temporary installation path

install for x64

* installing *source* package 'DMCHMM' ...
** testing if installed package can be loaded
Warning: replacing previous import 'IRanges::windows' by 'grDevices::windows' when loading 'DMCHMM'
* MD5 sums
packaged installation of 'DMCHMM' as DMCHMM_1.16.0.zip
* DONE (DMCHMM)
* installing to library 'C:/Users/biocbuild/bbs-3.14-bioc/R/library'
package 'DMCHMM' successfully unpacked and MD5 sums checked

Tests output

DMCHMM.Rcheck/tests_i386/testthat.Rout


R version 4.1.2 (2021-11-01) -- "Bird Hippie"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-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, 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

Attaching package: 'IRanges'

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

    windows

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 1.16.0, 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

Warning message:
replacing previous import 'IRanges::windows' by 'grDevices::windows' when loading 'DMCHMM' 
> 
> test_check("DMCHMM")

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  |=========                                                             |  12%
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  |====================================================                  |  75%
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  |=============================================================         |  88%
  |                                                                            
  |======================================================================| 100%

[ FAIL 0 | WARN 1 | SKIP 0 | PASS 1 ]
> 
> proc.time()
   user  system elapsed 
  11.73    0.73   12.45 

DMCHMM.Rcheck/tests_x64/testthat.Rout


R version 4.1.2 (2021-11-01) -- "Bird Hippie"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (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, 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

Attaching package: 'IRanges'

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

    windows

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 1.16.0, 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

Warning message:
replacing previous import 'IRanges::windows' by 'grDevices::windows' when loading 'DMCHMM' 
> 
> test_check("DMCHMM")

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  |                                                                            
  |=========                                                             |  12%
  |                                                                            
  |==================                                                    |  25%
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  |==========================                                            |  38%
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  |===================================                                   |  50%
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  |============================================                          |  62%
  |                                                                            
  |====================================================                  |  75%
  |                                                                            
  |=============================================================         |  88%
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  |======================================================================| 100%

[ FAIL 0 | WARN 1 | SKIP 0 | PASS 1 ]
> 
> proc.time()
   user  system elapsed 
  12.03    0.26   12.28 

Example timings

DMCHMM.Rcheck/examples_i386/DMCHMM-Ex.timings

nameusersystemelapsed
BSDMCs-class0.260.050.32
BSData-class0.210.060.26
cBSDMCs-method0.140.000.14
cBSData-method0.130.000.12
combine-method0.440.110.55
findDMCs-method60.95 0.2061.16
manhattanDMCs-method61.80 0.0861.89
methHMEM-method404
methHMMCMC-method62.64 0.0862.73
methLevels-method0.110.000.11
methReads-method0.120.010.14
methStates-method0.130.000.13
methVars-method0.090.000.09
qqDMCs-method62.44 0.2462.69
readBismark-method0.700.010.97
totalReads-method0.130.020.14

DMCHMM.Rcheck/examples_x64/DMCHMM-Ex.timings

nameusersystemelapsed
BSDMCs-class0.190.020.21
BSData-class0.150.000.16
cBSDMCs-method0.140.010.16
cBSData-method0.160.020.17
combine-method0.450.050.50
findDMCs-method64.53 0.1164.64
manhattanDMCs-method57.45 0.0457.50
methHMEM-method3.830.003.83
methHMMCMC-method52.97 0.1153.08
methLevels-method0.110.000.11
methReads-method0.090.020.11
methStates-method0.100.000.09
methVars-method0.090.000.10
qqDMCs-method53.98 0.0754.05
readBismark-method0.430.010.44
totalReads-method0.090.000.09