DOI: 10.18129/B9.bioc.methyvim    

This is the development version of methyvim; for the stable release version, see methyvim.

Differential Methylation Analysis with Targeted Minimum Loss-Based Estimates of Variable Importance Measures

Bioconductor version: Development (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 relationship among covariates and the outcome of interest. These tools can be applied to differential methylation at the level of CpG sites, with valid inference after multiple hypothesis testing.

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

Maintainer: Nima Hejazi <nhejazi at>

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HTML R Script methyvim: Variable Importance for DNA Methylation
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biocViews Clustering, DNAMethylation, DifferentialMethylation, MethylSeq, MethylationArray, Software
Version 1.1.0
License file LICENSE
Depends R (>= 3.4.0)
Imports stats, cluster, methods, ggplot2, gridExtra, superheat, wesanderson, magrittr, dplyr, gtools, tmle, future, doFuture, BiocParallel, BiocGenerics, 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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