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ocf

Ordered Correlation Forest

v1.0.3 · Feb 3, 2025 · GPL-3

Description

Machine learning estimator specifically optimized for predictive modeling of ordered non-numeric outcomes. 'ocf' provides forest-based estimation of the conditional choice probabilities and the covariates’ marginal effects. Under an "honesty" condition, the estimates are consistent and asymptotically normal and standard errors can be obtained by leveraging the weight-based representation of the random forest predictions. Please reference the use as Di Francesco (2025) <doi:10.1080/07474938.2024.2429596>.

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NOTE r-oldrel-macos-arm64

installed package size

installed size is  6.1Mb
  sub-directories of 1Mb or more:
    libs   5.3Mb
NOTE r-oldrel-macos-x86_64

installed package size

installed size is  6.0Mb
  sub-directories of 1Mb or more:
    libs   5.2Mb

Check History

NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
NOTE r-oldrel-macos-arm64

installed package size

installed size is  6.1Mb
  sub-directories of 1Mb or more:
    libs   5.3Mb
NOTE r-oldrel-macos-x86_64

installed package size

installed size is  6.0Mb
  sub-directories of 1Mb or more:
    libs   5.2Mb

Reverse Dependencies (1)

imports

Dependency Network

Dependencies Reverse dependencies Rcpp Matrix stringr orf glmnet ranger dplyr tidyr ggplot2 magrittr causalQual ocf

Version History

new 1.0.3 Mar 10, 2026
updated 1.0.3 ← 1.0.1 diff Feb 2, 2025
updated 1.0.1 ← 1.0.0 diff Sep 25, 2024
new 1.0.0 Sep 13, 2023