Author: Roman Hornung [aut, cre], Marvin N. Wright [ctb, cph]
Description: Implements diversity forests as described in an
upcoming paper by the author of the package.
This package is a fork of the R package 'ranger' (main author:
Marvin N. Wright) that implements random forests using an efficient C++
implementation. More precisely, 'diversityForest' was written by modifying
the code of 'ranger', version 0.11.0. Therefore, details on further
functionalities of the code that are not presented in the help pages of
'diversityForest' are found in the help pages of 'ranger' (version 0.11.0).
The code in the example sections of the 'diversityForest' manual can be used
as a template for all basic application scenarios with respect to
classification using univariate, binary splitting. Some function
arguments adopted from the 'ranger' package are not be useable
with diversity forests (for the current package version).

classification, regression and survival prediction using univariate,
binary splitting. Some function arguments adopted from the 'ranger'
package are not be useable with diversity forests (for the current

Title: Complex Split Procedures in Random Forests Through Candidate
Description: Implements interaction forests [1], which are specific diversity forests, and
the basic form of diversity forests that uses univariable, binary splitting [2].
Interaction forests (IFs) are ensembles of decision trees that model quantitative and
qualitative interaction effects using bivariable splitting. IFs come with the
Effect Importance Measure (EIM), which can be used to identify variable pairs that
feature quantitative and qualitative interaction effects with high predictive
relevance. IFs and EIM focus on well interpretable forms of interactions.
The package also offers plot functions for visualising the estimated forms of
Categorical, metric, and survival outcomes are supported.
This is a fork of the R package 'ranger' (main author: Marvin N. Wright) that
implements random forests using an efficient C++  implementation.
[1] Hornung, R. & Boulesteix, A.-L. (2021) Interaction Forests: Identifying and
exploiting interpretable quantitative and qualitative interaction effects.
Technical Report No. 237, Department of Statistics, University of Munich
[2] Hornung, R. (2020) Diversity Forests: Using split sampling to allow for complex
split procedures in random forest. Technical Report No. 234, Department of Statistics,
Imports: Rcpp (>= 0.11.2), Matrix, ggplot2, ggpubr, scales, nnet,
sgeostat, rms, MapGAM, gam, rlang, grDevices, RColorBrewer,