CRAN/E | aorsf

aorsf

Accelerated Oblique Random Forests

Installation

About

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.

Citation aorsf citation info
github.com/ropensci/aorsf
docs.ropensci.org/aorsf/
Bug report File report

Key Metrics

Version 0.1.2
R ≥ 3.6
Published 2024-01-15 73 days ago
Needs compilation? yes
License MIT
License File
CRAN checks aorsf results

Downloads

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Maintainer

Maintainer

Byron Jaeger

bjaeger@wakehealth.edu

Authors

Byron Jaeger

aut / cre

Nicholas Pajewski

ctb

Sawyer Welden

ctb

Christopher Jackson

rev

Marvin Wright

rev

Lukas Burk

rev

Material

README
NEWS
Reference manual
Package source

Vignettes

Introduction to aorsf
Tips to speed up computation
Out-of-bag predictions and evaluation
PD and ICE curves with ORSF

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

aorsf archive

Depends

R ≥ 3.6

Imports

collapse
data.table
lifecycle
R6
Rcpp
utils

Suggests

covr
ggplot2
glmnet
knitr
rmarkdown
survival
SurvMetrics
testthat ≥ 3.0.0
tibble
units

LinkingTo

Rcpp
RcppArmadillo

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