Package: frailtyMMpen 1.2.1

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
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frailtyMMpen_1.2.1.tgz(r-4.4-x86_64)frailtyMMpen_1.2.1.tgz(r-4.4-arm64)frailtyMMpen_1.2.1.tgz(r-4.3-x86_64)frailtyMMpen_1.2.1.tgz(r-4.3-arm64)
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frailtyMMpen.pdf |frailtyMMpen.html
frailtyMMpen/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/heilokchow/frailtymmpen/issues

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

On CRAN:

2.70 score 221 downloads 3 exports 8 dependencies

Last updated 1 years agofrom:4baf5ffb25. Checks:OK: 4 NOTE: 5. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 12 2024
R-4.5-win-x86_64NOTENov 12 2024
R-4.5-linux-x86_64NOTENov 12 2024
R-4.4-win-x86_64NOTENov 12 2024
R-4.4-mac-x86_64NOTENov 12 2024
R-4.4-mac-aarch64NOTENov 12 2024
R-4.3-win-x86_64OKNov 12 2024
R-4.3-mac-x86_64OKNov 12 2024
R-4.3-mac-aarch64OKNov 12 2024

Exports:eventfrailtyMMfrailtyMMpen

Dependencies:latticeMatrixmgcvnlmenumDerivRcppRcppGSLsurvival