Package: orf 0.1.4.9000

orf: Ordered Random Forests

An implementation of the Ordered Forest estimator as developed in Lechner & Okasa (2019) <arxiv:1907.02436>. The Ordered Forest flexibly estimates the conditional probabilities of models with ordered categorical outcomes (so-called ordered choice models). Additionally to common machine learning algorithms the 'orf' package provides functions for estimating marginal effects as well as statistical inference thereof and thus provides similar output as in standard econometric models for ordered choice. The core forest algorithm relies on the fast C++ forest implementation from the 'ranger' package (Wright & Ziegler, 2017) <arxiv:1508.04409>.

Authors:Gabriel Okasa [aut, cre], Michael Lechner [ctb]

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orf.pdf |orf.html
orf/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/okasag/orf/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

5.07 score 12 stars 1 packages 22 scripts 190 downloads 3 mentions 2 exports 32 dependencies

Last updated 2 years agofrom:a1fcc68279. Checks:ERROR: 9. Indexed: yes.

TargetResultDate
Doc / VignettesFAILOct 08 2024
R-4.5-win-x86_64ERROROct 08 2024
R-4.5-linux-x86_64ERROROct 08 2024
R-4.4-win-x86_64ERROROct 08 2024
R-4.4-mac-x86_64ERROROct 08 2024
R-4.4-mac-aarch64ERROROct 08 2024
R-4.3-win-x86_64ERROROct 08 2024
R-4.3-mac-x86_64ERROROct 08 2024
R-4.3-mac-aarch64ERROROct 08 2024

Exports:marginsorf

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6rangerRColorBrewerRcppRcppEigenrlangscalestibbleutf8vctrsviridisLitewithrxtable