Package: aorsf 0.1.6

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]

aorsf_0.1.6.tar.gz
aorsf_0.1.6.zip(r-4.7)aorsf_0.1.6.zip(r-4.6)aorsf_0.1.6.zip(r-4.5)
aorsf_0.1.6.tgz(r-4.6-x86_64)aorsf_0.1.6.tgz(r-4.6-arm64)aorsf_0.1.6.tgz(r-4.5-x86_64)aorsf_0.1.6.tgz(r-4.5-arm64)
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aorsf_0.1.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
aorsf/json (API)

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

Reviews:rOpenSci Software Review #532

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

Pkgdown/docs site:https://docs.ropensci.org

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:

Conda:

data-scienceobliquerandom-forestsurvivalopenblascppopenmp

9.60 score 59 stars 3 packages 105 scripts 3.0k downloads 28 exports 8 dependencies

Last updated from:085db7062d (on main). Checks:14 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK218
linux-devel-x86_64OK281
pkgdown docsOK302
source / vignettesOK402
linux-release-arm64OK238
linux-release-x86_64OK295
macos-release-arm64OK205
macos-release-x86_64OK287
macos-oldrel-arm64OK141
macos-oldrel-x86_64OK457
windows-develOK248
windows-releaseOK251
windows-oldrelOK221
wasm-releaseOK232

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.tablelifecycleR6RcppRcppArmadillorlang

Out-of-bag predictions and evaluation
Out-of-bag data | Out-of-bag predictions and error | Monitoring out-of-bag error | User-supplied out-of-bag evaluation functions | Specific instructions on user-supplied functions | Notes
Rendered fromoobag.Rmdusingknitr::rmarkdown

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

PD and ICE curves with ORSF
Partial dependence (PD) | Classification | Regression | Survival | One variable, moving horizon | Multiple variables, jointly | Find interactions using PD | Individual conditional expectations (ICE) | Visualizing ICE curves | Limitations of PD | References
Rendered frompd.Rmdusingknitr::rmarkdown

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

Tips to speed up computation
Go faster | Don't specify a control | Use n_thread | Do less | Show progress | Don't wait. Estimate!
Rendered fromfast.Rmdusingknitr::rmarkdown

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

Introduction to aorsf
Background | Oblique RFs for survival, classification, and regression | Interpretation | Variable importance | Partial dependence (PD) | Individual conditional expectations (ICE) | What about the original ORSF? | aorsf and other machine learning software | Learning more
Rendered fromaorsf.Rmdusingknitr::rmarkdown

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

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