exprso: Rapid Deployment of Machine Learning Algorithms

Supervised machine learning has an increasingly important role in data analysis. This package introduces a framework for rapidly building and deploying supervised machine learning in a high-throughput manner. This package provides a user-friendly interface that empowers investigators to execute state-of-the-art binary and multi-class classification, as well as regression, with minimal programming experience necessary.

Version: 0.2.7
Depends: R (≥ 3.2.2), kernlab
Imports: cluster, MASS, e1071, lattice, methods, nnet, plyr, stats, randomForest, ROCR, sampling
Suggests: Biobase, edgeR, GEOquery, h2o, knitr, limma, magrittr, mRMRe, pathClass, propr, rmarkdown, testthat
Published: 2017-12-14
Author: Thomas Quinn [aut, cre], Daniel Tylee [ctb]
Maintainer: Thomas Quinn <contacttomquinn at gmail.com>
BugReports: http://github.com/tpq/exprso/issues
License: GPL-2
URL: http://github.com/tpq/exprso
NeedsCompilation: no
Citation: exprso citation info
Materials: README NEWS
CRAN checks: exprso results

Downloads:

Reference manual: exprso.pdf
Vignettes: 1. An Introduction to the exprso Package
2. Advanced Topics for the exprso Package
3. Use Disclaimer, Please Read
Package source: exprso_0.2.7.tar.gz
Windows binaries: r-devel: exprso_0.2.6.zip, r-release: exprso_0.2.6.zip, r-oldrel: exprso_0.2.6.zip
OS X El Capitan binaries: r-release: exprso_0.2.7.tgz
OS X Mavericks binaries: r-oldrel: exprso_0.2.6.tgz
Old sources: exprso archive

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