Package: edarf
Version: 1.1.1
Date: 2017-03-05
Title: Exploratory Data Analysis using Random Forests
Description: 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.
Author: Zachary M. Jones <zmj@zmjones.com> and Fridolin Linder
    <fridolin.linder@gmail.com>
Maintainer: Zachary M. Jones <zmj@zmjones.com>
License: MIT + file LICENSE
Depends: R (>= 2.10)
Imports: data.table, ggplot2, mmpf
Suggests: party, randomForest, randomForestSRC, ranger, testthat,
        rmarkdown, knitr
LazyData: true
BugReports: https://github.com/zmjones/edarf
RoxygenNote: 6.0.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2017-03-06 03:27:45 UTC; zmjones
Repository: CRAN
Date/Publication: 2017-03-06 08:28:57
