Package: methyvim
Title: Differential Methylation Analysis with Targeted Minimum
        Loss-Based Estimates of Variable Importance Measures
Version: 1.0.0
Authors@R: c(
  person("Nima", "Hejazi", email = "nhejazi@berkeley.edu",
    role = c("aut", "cre", "cph")),
  person("Rachael", "Phillips", email = "rachaelvphillips@berkeley.edu",
    role = "ctb"),
  person("Alan", "Hubbard", email = "hubbard@berkeley.edu", role = "ctb"),
  person("Mark", "van der Laan", email = "laan@stat.berkeley.edu",
    role = "aut")
  )
Author: Nima Hejazi [aut, cre, cph], Rachael Phillips [ctb], Alan Hubbard [ctb],
  Mark van der Laan [aut]
Maintainer: Nima Hejazi <nhejazi@berkeley.edu>
Description: 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.
Depends: R (>= 3.4.0)
License: file LICENSE
URL: https://github.com/nhejazi/methyvim
BugReports: https://github.com/nhejazi/methyvim/issues
Encoding: UTF-8
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
VignetteBuilder: knitr
RoxygenNote: 6.0.1
biocViews: Clustering, DNAMethylation, DifferentialMethylation,
        MethylationArray, MethylSeq
NeedsCompilation: no
Packaged: 2017-10-31 02:24:14 UTC; biocbuild
Built: R 3.4.2; ; 2017-10-31 06:15:55 UTC; windows
