Package: icRSF 1.2

Hui Xu

icRSF: A Modified Random Survival Forest Algorithm

Implements a modification to the Random Survival Forests algorithm for obtaining variable importance in high dimensional datasets. The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic.

Authors:Hui Xu and Raji Balasubramanian

icRSF_1.2.tar.gz
icRSF_1.2.tar.gz(r-4.6-arm64)icRSF_1.2.tar.gz(r-4.6-x86_64)icRSF_1.2.tar.gz(r-4.5-arm64)icRSF_1.2.tar.gz(r-4.5-x86_64)
icRSF_1.2.tgz(r-4.5-emscripten)
icRSF.pdf |icRSF.html
icRSF/json (API)

# Install 'icRSF' in R:
install.packages('icRSF', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • pheno - A longitudinal data with diagnostic results for pre-determined time
  • Xmat - A covariate matrix

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

1.00 score 4 scripts 252 downloads 3 exports 2 dependencies

Last updated from:0af2f2cc67. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK114
linux-devel-x86_64OK132
source / vignettesOK163
linux-release-arm64OK121
linux-release-x86_64OK129
wasm-releaseOK102

Exports:icrsfsimouttreebuilder

Dependencies:icensmisRcpp