Package: diversityForest 0.4.0

diversityForest: Innovative Complex Split Procedures in Random Forests Through Candidate Split Sampling

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 interaction effects. 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. References: [1] Hornung, R. & Boulesteix, A.-L. (2022) Interaction Forests: Identifying and exploiting interpretable quantitative and qualitative interaction effects. Computational Statistics & Data Analysis 171:107460, <doi:10.1016/j.csda.2022.107460>. [2] Hornung, R. (2022) Diversity forests: Using split sampling to enable innovative complex split procedures in random forests. SN Computer Science 3(2):1, <doi:10.1007/s42979-021-00920-1>.

Authors:Roman Hornung [aut, cre], Marvin N. Wright [ctb, cph]

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diversityForest.pdf |diversityForest.html
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NEWS

# Install 'diversityForest' in R:
install.packages('diversityForest', repos = c('https://romanhornung.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/romanhornung/diversityforest/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • stock - Data on stock prices of aerospace companies
  • zoo - Data on biological species

On CRAN:

6 exports 0.64 score 124 dependencies 5 scripts 332 downloads

Last updated 2 years agofrom:2d1c7273f1. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 07 2024
R-4.5-win-x86_64OKSep 07 2024
R-4.5-linux-x86_64OKSep 07 2024
R-4.4-win-x86_64OKSep 07 2024
R-4.4-mac-x86_64OKSep 07 2024
R-4.4-mac-aarch64OKSep 07 2024
R-4.3-win-x86_64OKAug 08 2024
R-4.3-mac-x86_64OKAug 08 2024
R-4.3-mac-aarch64OKAug 08 2024

Exports:divforimportanceinteractionforplotEffectsplotPairtunedivfor

Dependencies:abindbackportsbase64encbootbroombslibcachemcarcarDatacheckmateclassclassIntcliclustercodetoolscolorspacecorrplotcowplotcpp11data.tableDBIDerivdigestdoBydplyre1071evaluatefansifarverfastmapfontawesomeforeachforeignFormulafsgamgenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelifecyclelme4magrittrMapGAMMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrmultcompmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestPBSmappingpillarpkgconfigpolsplinepolynomproxypurrrquantregR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdownrmsrpartrstatixrstudioapis2sandwichsassscalessfsgeostatspSparseMstringistringrsurvivalTH.datatibbletidyrtidyselecttinytexunitsutf8vctrsviridisviridisLitewithrwkxfunyamlzoo