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>.

Version: 1.2.1
Depends: R (≥ 3.5.0), survival, numDeriv, mgcv
Imports: Rcpp (≥ 1.0.8), utils, graphics, stats
LinkingTo: Rcpp, RcppGSL
Published: 2023-08-08
Author: Xifen Huang [aut], Yunpeng Zhou [aut, cre], Jinfeng Xu [ctb]
Maintainer: Yunpeng Zhou <u3514104 at connect.hku.hk>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: frailtyMMpen results

Documentation:

Reference manual: frailtyMMpen.pdf

Downloads:

Package source: frailtyMMpen_1.2.1.tar.gz
Windows binaries: r-devel: frailtyMMpen_1.2.1.zip, r-release: frailtyMMpen_1.2.1.zip, r-oldrel: frailtyMMpen_1.2.1.zip
macOS binaries: r-release (arm64): frailtyMMpen_1.2.1.tgz, r-oldrel (arm64): frailtyMMpen_1.2.1.tgz, r-release (x86_64): frailtyMMpen_1.2.1.tgz
Old sources: frailtyMMpen archive

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