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.

Maintainer: Zachary M. Jones <zmj at zmjones.com>

Author(s): Zachary M. Jones <zmj at zmjones.com> and Fridolin Linder <fridolin.linder at gmail.com>

Install package and any missing dependencies by running this line in your R console:

install.packages("edarf")

Depends R (>= 2.10)
Imports data.table, ggplot2, mmpf
Suggests party, randomForest, randomForestSRC, ranger, testthat, rmarkdown, knitr
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Package edarf
Materials
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Version 1.1.1
Published 2017-03-06
License MIT + file LICENSE
BugReports https://github.com/zmjones/edarf
SystemRequirements
NeedsCompilation no
Citation
CRAN checks edarf check results
Package source edarf_1.1.1.tar.gz