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]

# Install 'frailtyMMpen' in R:

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

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

Uses libs:

gsl– GNU Scientific Library (GSL)
c++– GNU Standard C++ Library v3

Datasets:

hdCLdata - Simulated High-dimensional Clustered data
simdataCL - Simulated Clustered data
simdataME - Simulated Multiple Event data
simdataRE - Simulated Recurrent Event data

On CRAN:

gsl cpp

2.70 score 296 downloads 3 exports 8 dependencies

Last updated from:4baf5ffb25. Checks:13 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	NOTE	191
linux-devel-x86_64	NOTE	146
source / vignettes	OK	229
linux-release-arm64	NOTE	175
linux-release-x86_64	NOTE	170
macos-release-arm64	NOTE	140
macos-release-x86_64	NOTE	331
macos-oldrel-arm64	NOTE	113
macos-oldrel-x86_64	NOTE	386
windows-devel-arm64	NOTE	341
windows-devel-x86_64	NOTE	150
windows-release-arm64	NOTE	354
windows-release-x86_64	NOTE	105
windows-oldrel-x86_64	NOTE	109
wasm-release	OK	143

Exports:event frailtyMM frailtyMMpen

Dependencies:lattice Matrix mgcv nlme numDeriv Rcpp RcppGSL survival

Help page	Topics
retrieve the coefficients under given tuning parameter	coef.fpen
#' cluster function #' #' @param x name from original dataframe which specifies the cluster or object id. #' @description Specify cluster id for clustered data or object id for recurrent data in the input 'formula'. #' @return No return value, called to construct 'formula'. #' @export cluster <- function(x) x event function	event
Fitting frailty models with clustered, multi-event and recurrent data using MM algorithm	frailtyMM
Fitting penalized frailty models with clustered, multi-event and recurrent data using MM algorithm	frailtyMMpen
Simulated High-dimensional Clustered data	hdCLdata
Plot the baseline hazard or the predicted hazard based on the new data	plot.fmm
Plot the regularization path	plot.fpen
Estimate the baseline hazard or the predict hazard rate based on the new data for non-penalized regression	predict.fmm
Estimate the baseline hazard or the predict hazard rate based on the new data for penalized regression	predict.fpen
print a non-penalized regression object	print.fmm
print a penalized regression object	print.fpen
Simulated Clustered data	simdataCL
Simulated Multiple Event data	simdataME
Simulated Recurrent Event data	simdataRE
Provide the summary for the model fitting	summary.fmm

Package: frailtyMMpen 1.2.1

frailtyMMpen: Efficient Algorithm for High-Dimensional Frailty Model

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