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 |
| 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_0.2.7.zip, r-release: exprso_0.2.7.zip, r-oldrel: exprso_0.2.7.zip |
| OS X El Capitan binaries: | r-release: exprso_0.2.7.tgz |
| OS X Mavericks binaries: | r-oldrel: exprso_0.2.7.tgz |
| Old sources: | exprso archive |
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