doi:10.1214/08-AOAS169>). Regularized Cox proportional hazard models (Simon, 2016 <doi:10.18637/jss.v039.i05>) are used to identify optimal linear combinations of input variables.">

obliqueRSF: Oblique Random Forests for Right-Censored Time-to-Event Data (original) (raw)

Oblique random survival forests incorporate linear combinations of input variables into random survival forests (Ishwaran, 2008 <doi:10.1214/08-AOAS169>). Regularized Cox proportional hazard models (Simon, 2016 <doi:10.18637/jss.v039.i05>) are used to identify optimal linear combinations of input variables.

Version: 0.1.2
Depends: R (≥ 3.5.0)
Imports: Rcpp, pec, data.table, stats, missForest, purrr, glmnet, survival, dplyr, rlang, prodlim, ggthemes, tidyr, ggplot2, scales
LinkingTo: Rcpp, RcppArmadillo
Published: 2022-08-28
DOI: 10.32614/CRAN.package.obliqueRSF
Author: Byron Jaeger [aut, cre]
Maintainer: Byron Jaeger
License: GPL-3
NeedsCompilation: yes
Materials: README
CRAN checks: obliqueRSF results

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