Package: exprso
Title: Rapid Implementation of Machine Learning Algorithms for Genomic
        Data
Version: 0.1.7
URL: http://github.com/tpq/exprso
BugReports: http://github.com/tpq/exprso/issues
Authors@R: c(
    person("Thomas", "Quinn", email = "contacttomquinn@gmail.com", role = c("aut", "cre")),
    person("Daniel", "Tylee", email = "dantylee@gmail.com", role = "ctb")
    )
Description: Supervised machine learning has an increasingly important role in biological
    studies. However, the sheer complexity of classification pipelines poses a significant
    barrier to the expert biologist unfamiliar with machine learning. Moreover,
    many biologists lack the time or technical skills necessary to establish their own
    pipelines. This package introduces a framework for the rapid implementation of
    high-throughput supervised machine learning built with the biologist user in mind.
    Written by biologists, for biologists, this package provides a user-friendly interface
    that empowers investigators to execute state-of-the-art binary and multi-class
    classification, including deep learning, with minimal programming
    experience necessary.
License: GPL-2
LazyData: TRUE
VignetteBuilder: knitr
RoxygenNote: 5.0.1
Imports: cluster, MASS, e1071, lattice, methods, mRMRe, nnet,
        pathClass, plyr, stats, randomForest, ROCR, sampling
Depends: R (>= 3.2.2), kernlab
Suggests: Biobase, GEOquery, h2o, golubEsets, knitr, limma, magrittr,
        rmarkdown, testthat
NeedsCompilation: no
Packaged: 2016-09-27 07:00:17 UTC; thom
Author: Thomas Quinn [aut, cre],
  Daniel Tylee [ctb]
Maintainer: Thomas Quinn <contacttomquinn@gmail.com>
Repository: CRAN
Date/Publication: 2016-09-27 16:39:46
