Package: PPSFS 0.1.0

Zengchao Xu

PPSFS: Partial Profile Score Feature Selection in High-Dimensional Generalized Linear Interaction Models

This is an implementation of the partial profile score feature selection (PPSFS) approach to generalized linear (interaction) models. The PPSFS is highly scalable even for ultra-high-dimensional feature space. See the paper by Xu, Luo and Chen (2021, <doi:10.4310/21-SII706>).

Authors:Zengchao Xu [aut, cre], Shan Luo [aut], Zehua Chen [aut]

PPSFS_0.1.0.tar.gz
PPSFS_0.1.0.zip(r-4.5)PPSFS_0.1.0.zip(r-4.4)PPSFS_0.1.0.zip(r-4.3)
PPSFS_0.1.0.tgz(r-4.4-x86_64)PPSFS_0.1.0.tgz(r-4.4-arm64)PPSFS_0.1.0.tgz(r-4.3-x86_64)PPSFS_0.1.0.tgz(r-4.3-arm64)
PPSFS_0.1.0.tar.gz(r-4.5-noble)PPSFS_0.1.0.tar.gz(r-4.4-noble)
PPSFS_0.1.0.tgz(r-4.4-emscripten)PPSFS_0.1.0.tgz(r-4.3-emscripten)
PPSFS.pdf |PPSFS.html
PPSFS/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/paradoxical-rhapsody/ppsfs/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

2.70 score 1 stars 149 downloads 2 exports 9 dependencies

Last updated 3 years agofrom:25ecc372b8. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 22 2024
R-4.5-win-x86_64NOTENov 22 2024
R-4.5-linux-x86_64NOTENov 22 2024
R-4.4-win-x86_64NOTENov 22 2024
R-4.4-mac-x86_64NOTENov 22 2024
R-4.4-mac-aarch64NOTENov 22 2024
R-4.3-win-x86_64NOTENov 22 2024
R-4.3-mac-x86_64NOTENov 22 2024
R-4.3-mac-aarch64NOTENov 22 2024

Exports:ppsfsppsfsi

Dependencies:brglm2enrichwithlatticeMASSMatrixnnetnumDerivRcppRcppArmadillo