Package: frailtyMMpen 1.2.1

Yunpeng Zhou

frailtyMMpen: Efficient Algorithm for High-Dimensional Frailty Model

The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers, Huang, Xu and Zhou (2022) <doi:10.3390/math10040538>, Huang, Xu and Zhou (2023) <doi:10.1177/09622802221133554>.

Authors:Xifen Huang [aut], Yunpeng Zhou [aut, cre], Jinfeng Xu [ctb]

frailtyMMpen_1.2.1.tar.gz
frailtyMMpen_1.2.1.tar.gz(r-4.5-noble)frailtyMMpen_1.2.1.tar.gz(r-4.4-noble)
frailtyMMpen_1.2.1.tgz(r-4.4-emscripten)frailtyMMpen_1.2.1.tgz(r-4.3-emscripten)
frailtyMMpen.pdf |frailtyMMpen.html
frailtyMMpen/json (API)
NEWS

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

Peer review:

Uses libs:
  • gsl– GNU Scientific Library (GSL)
  • c++– GNU Standard C++ Library v3
Datasets:

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

gslcpp

1.70 score 235 downloads 4 exports 8 dependencies

Last updated 1 years agofrom:a1d0a64d78. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 03 2024
R-4.5-linux-x86_64NOTEDec 03 2024

Exports:clustereventfrailtyMMfrailtyMMpen

Dependencies:latticeMatrixmgcvnlmenumDerivRcppRcppGSLsurvival