methyvim: Targeted, Robust, and Model-free Differential Methylation Analysis

This package provides facilities for differential methylation analysis based on variable importance measures (VIMs), a class of statistical target parameters that arise in causal inference. The estimation and inference procedures provided are nonparametric, relying on ensemble machine learning to flexibly assess functional relationships among covariates and the outcome of interest. These tools can be applied to differential methylation at the level of CpG sites, to obtain valid statistical inference even after corrections for multiple hypothesis testing.

Package details

AuthorNima Hejazi [aut, cre, cph] (<>), Rachael Phillips [ctb] (<>), Mark van der Laan [aut, ths] (<>), Alan Hubbard [ctb, ths] (<>)
Bioconductor views Clustering DNAMethylation DifferentialMethylation MethylSeq MethylationArray
MaintainerNima Hejazi <>
Licensefile LICENSE
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
if (!requireNamespace("BiocManager", quietly = TRUE))


Try the methyvim package in your browser

Any scripts or data that you put into this service are public.

methyvim documentation built on Nov. 8, 2020, 11:11 p.m.