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:
frailtyMMpen_1.2.1.tar.gz
frailtyMMpen_1.2.1.zip(r-4.7-x86_64)frailtyMMpen_1.2.1.zip(r-4.7-arm64)frailtyMMpen_1.2.1.zip(r-4.6-x86_64)frailtyMMpen_1.2.1.zip(r-4.6-arm64)
frailtyMMpen_1.2.1.tar.gz(r-4.7-arm64)frailtyMMpen_1.2.1.tar.gz(r-4.7-x86_64)frailtyMMpen_1.2.1.tar.gz(r-4.6-arm64)frailtyMMpen_1.2.1.tar.gz(r-4.6-x86_64)
frailtyMMpen_1.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
frailtyMMpen/json (API)
| # Install 'frailtyMMpen' in R: |
| install.packages('frailtyMMpen', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:a1d0a64d78. Checks:8 NOTE, 2 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | NOTE | 115 | ||
| linux-devel-x86_64 | NOTE | 154 | ||
| source / vignettes | OK | 163 | ||
| linux-release-arm64 | NOTE | 111 | ||
| linux-release-x86_64 | NOTE | 117 | ||
| windows-devel-arm64 | NOTE | 207 | ||
| windows-devel-x86_64 | NOTE | 126 | ||
| windows-release-arm64 | NOTE | 199 | ||
| windows-release-x86_64 | NOTE | 124 | ||
| wasm-release | OK | 106 |
Exports:clustereventfrailtyMMfrailtyMMpen
Dependencies:latticeMatrixmgcvnlmenumDerivRcppRcppGSLsurvival
