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### Running command:
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###   /Library/Frameworks/R.framework/Resources/bin/R CMD build --keep-empty-dirs --no-resave-data DMCHMM
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* checking for file ‘DMCHMM/DESCRIPTION’ ... OK
* preparing ‘DMCHMM’:
* checking DESCRIPTION meta-information ... OK
* installing the package to build vignettes
* creating vignettes ... ERROR
--- re-building ‘DMCHMM.Rmd’ using rmarkdown
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'MatrixGenerics'

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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'

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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'

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Processing sample blk.BCU173_TC_BS_1 ... 
Read 24421 records


Processing sample blk.BCU1568_BC_BS_1 ... 
Read 23710 records


Processing sample blk.BCU551_Mono_BS_1 ... 
Read 23541 records

Quitting from lines 122-124 (DMCHMM.Rmd) 
Error: processing vignette 'DMCHMM.Rmd' failed with diagnostics:
error in evaluating the argument 'x' in selecting a method for function 'as.matrix': values must be length 1,
 but FUN(X[[1]]) result is length 2
--- failed re-building ‘DMCHMM.Rmd’

SUMMARY: processing the following file failed:
  ‘DMCHMM.Rmd’

Error: Vignette re-building failed.
Execution halted