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

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

Peer review:

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

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • pbc_orsf - Mayo Clinic Primary Biliary Cholangitis Data
  • penguins_orsf - Size measurements for adult foraging penguins near Palmer Station, Antarctica

On CRAN:

data-scienceobliquerandom-forestsurvival

28 exports 32 stars 3.36 score 9 dependencies 1 dependents 1.5k downloads

Last updated 17 days agofrom:d5c115880d4421eea31c5c0b8e453ca5c0dcf5ac (via 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

Introduction to aorsf

Rendered fromaorsf.Rmdusingknitr::rmarkdownon Jun 02 2024.

Last update: 2023-12-20
Started: 2022-01-11

Out-of-bag predictions and evaluation

Rendered fromoobag.Rmdusingknitr::rmarkdownon Jun 02 2024.

Last update: 2024-01-19
Started: 2022-04-01

PD and ICE curves with ORSF

Rendered frompd.Rmdusingknitr::rmarkdownon Jun 02 2024.

Last update: 2024-01-18
Started: 2022-01-12

Tips to speed up computation

Rendered fromfast.Rmdusingknitr::rmarkdownon Jun 02 2024.

Last update: 2024-01-05
Started: 2023-10-09

Readme and manuals

Help Manual

Help pageTopics
Coerce to data.tableas.data.table.orsf_summary_uni
Oblique Random Forestsorsf orsf_train
Oblique random forest controlorsf_control orsf_control_classification orsf_control_regression orsf_control_survival
Cox regression ORSF controlorsf_control_cph
Custom ORSF controlorsf_control_custom
Accelerated ORSF controlorsf_control_fast
Penalized Cox regression ORSF controlorsf_control_net
Individual Conditional Expectationsorsf_ice_inb orsf_ice_new orsf_ice_oob
Partial dependenceorsf_pd_inb orsf_pd_new orsf_pd_oob
Scale input dataorsf_scale_cph orsf_unscale_cph
Univariate summaryorsf_summarize_uni
Estimate training timeorsf_time_to_train
Update Forest Parametersorsf_update
Variable Importanceorsf_vi orsf_vi_anova orsf_vi_negate orsf_vi_permute
Variable Interactionsorsf_vint
Variable selectionorsf_vs
Mayo Clinic Primary Biliary Cholangitis Datapbc_orsf
Size measurements for adult foraging penguins near Palmer Station, Antarcticapenguins_orsf
Automatic variable values for dependencepred_spec_auto
Prediction for ObliqueForest Objectspredict.ObliqueForest
Inspect Forest Parametersprint.ObliqueForest
Print ORSF summaryprint.orsf_summary_uni