DOI: 10.18129/B9.bioc.methyvim    

This package is for version 3.7 of Bioconductor; for the stable, up-to-date release version, see methyvim.

Targeted Variable Importance for Differential Methylation Analysis

Bioconductor version: 3.7

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.

Author: Nima Hejazi [aut, cre, cph], Mark van der Laan [aut, ths], Alan Hubbard [ctb, ths], Rachael Phillips [ctb]

Maintainer: Nima Hejazi <nhejazi at>

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biocViews Clustering, DNAMethylation, DifferentialMethylation, MethylSeq, MethylationArray, Software
Version 1.2.0
License file LICENSE
Depends R (>= 3.4.0)
Imports stats, cluster, methods, ggplot2, ggsci, gridExtra, superheat, dplyr, gtools, tmle, future, doFuture, BiocGenerics, BiocParallel, SummarizedExperiment, GenomeInfoDb, bumphunter, IRanges, limma, minfi
Suggests testthat, knitr, rmarkdown, BiocStyle, SuperLearner, earth, nnet, gam, arm, snow, parallel, BatchJobs, minfiData, methyvimData
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