Package: aorsf 0.1.5.9001

Byron Jaeger

aorsf: Accelerated Oblique Random Forests

Fit, interpret, and compute predictions with oblique random forests. Includes support for partial dependence, variable importance, passing customized functions for variable importance and identification of linear combinations of features. Methods for the oblique random survival forest are described in Jaeger et al., (2023) <doi:10.1080/10618600.2023.2231048>.

Authors:Byron Jaeger [aut, cre], Nicholas Pajewski [ctb], Sawyer Welden [ctb], Christopher Jackson [rev], Marvin Wright [rev], Lukas Burk [rev]

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NEWS

Peer review:

Bug tracker:https://github.com/ropensci/aorsf/issues

On CRAN:

data-scienceobliquerandom-forestsurvival

28 exports 50 stars 3.79 score 9 dependencies 1 dependents 1.6k downloads

Last updated 22 days agofrom:6b13ee73e3 (on main)

Exports:orsforsf_controlorsf_control_classificationorsf_control_cphorsf_control_customorsf_control_fastorsf_control_netorsf_control_regressionorsf_control_survivalorsf_ice_inborsf_ice_neworsf_ice_ooborsf_pd_inborsf_pd_neworsf_pd_ooborsf_scale_cphorsf_summarize_uniorsf_time_to_trainorsf_trainorsf_unscale_cphorsf_updateorsf_viorsf_vi_anovaorsf_vi_negateorsf_vi_permuteorsf_vintorsf_vspred_spec_auto

Dependencies:clicollapsedata.tablegluelifecycleR6RcppRcppArmadillorlang