Package: BioMM
Type: Package
Title: BioMM: Biological-informed Multi-stage Machine learning
        framework for phenotype prediction using omics data
Version: 1.2.0
Date: 2019-08-08
Author: Junfang Chen and Emanuel Schwarz
Maintainer: Junfang Chen <junfang.chen33@gmail.com>
Description: The identification of reproducible biological patterns from 
	high-dimensional omics data is a key factor in understanding the biology 
	of complex disease or traits. Incorporating prior biological knowledge 
	into machine learning is an important step in advancing such research. 
	We have proposed a biologically informed multi-stage machine learing 
	framework termed BioMM specifically for phenotype prediction based on 
	omics-scale data where we can evaluate different machine learning models 
	with prior biological meta information. 
Imports: stats, utils, grDevices, lattice, BiocParallel, glmnet, rms,
        nsprcomp, ranger, e1071, variancePartition, ggplot2, pROC,
        vioplot, CMplot
Depends: R (>= 3.6)
Suggests: BiocStyle, knitr, RUnit, BiocGenerics
VignetteBuilder: knitr
biocViews: Genetics, Classification, Regression, Pathways, GO, Software
Encoding: UTF-8
LazyData: true
License: GPL-3
RoxygenNote: 6.1.1
git_url: https://git.bioconductor.org/packages/BioMM
git_branch: RELEASE_3_10
git_last_commit: b509122
git_last_commit_date: 2019-10-29
Date/Publication: 2019-10-29
NeedsCompilation: no
Packaged: 2019-10-30 03:31:47 UTC; biocbuild
Built: R 3.6.1; ; 2019-11-01 19:47:05 UTC; unix
