edarf: Exploratory Data Analysis using Random Forests

Functions useful for exploratory data analysis using random forests which can be used to compute multivariate partial dependence, observation, class, and variable-wise marginal and joint permutation importance as well as observation-specific measures of distance (supervised or unsupervised). All of the aforementioned functions are accompanied by 'ggplot2' plotting functions.

Version: 1.0.0
Depends: R (≥ 2.10)
Imports: foreach, iterators, ggplot2, assertthat, reshape2, plyr
Suggests: party, randomForest, randomForestSRC, doParallel, testthat, rmarkdown, knitr
Published: 2016-10-25
Author: Zachary M. Jones and Fridolin Linder
Maintainer: Zachary M. Jones <zmj at zmjones.com>
BugReports: https://github.com/zmjones/edarf
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: edarf results

Downloads:

Reference manual: edarf.pdf
Vignettes: Exploratory Data Analysis Using Random Forests
Package source: edarf_1.0.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
OS X Mavericks binaries: r-release: not available, r-oldrel: not available

Linking:

Please use the canonical form https://CRAN.R-project.org/package=edarf to link to this page.