Package: DMCFB
Type: Package
Title: Differentially Methylated Cytosines via a Bayesian Functional
        Approach
Version: 1.0.0
Author: Farhad Shokoohi
Maintainer: Farhad Shokoohi <shokoohi@icloud.com>
Description: DMCFB is a pipeline for identifying differentially methylated 
    cytosines using a Bayesian functional regression model in bisulfite 
    sequencing data. By using a functional regression data model, it tries to 
    capture position-specific, group-specific and other covariates-specific 
    methylation patterns as well as spatial correlation patterns and unknown 
    underlying models of methylation data. It is robust and flexible with 
    respect to the true underlying models and inclusion of any covariates, and 
    the missing values are imputed using spatial correlation between positions 
    and samples. A Bayesian approach is adopted for estimation and inference in 
    the proposed method.
Depends: R (>= 3.6.0), SummarizedExperiment, methods, S4Vectors,
        BiocParallel, GenomicRanges, IRanges
Imports: utils, stats, speedglm, MASS, data.table, splines, arm,
        rtracklayer, benchmarkme, tibble, matrixStats, fastDummies,
        graphics
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
biocViews: DifferentialMethylation, Sequencing, Coverage, Bayesian,
        Regression
License: GPL-3
Encoding: UTF-8
LazyData: true
BugReports: https://github.com/shokoohi/DMCFB/issues
RoxygenNote: 6.1.1
git_url: https://git.bioconductor.org/packages/DMCFB
git_branch: RELEASE_3_10
git_last_commit: 834d392
git_last_commit_date: 2019-10-29
Date/Publication: 2019-10-29
NeedsCompilation: no
Packaged: 2019-10-30 03:53:23 UTC; biocbuild
Built: R 3.6.1; ; 2019-11-03 21:53:04 UTC; unix
