CRAN/E | glmmPen

glmmPen

High Dimensional Penalized Generalized Linear Mixed Models (pGLMM)

Installation

About

Fits high dimensional penalized generalized linear mixed models using the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. The purpose of the package is to perform variable selection on both the fixed and random effects simultaneously for generalized linear mixed models. The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm is described in Rashid et al. (2020) doi:10.1080/01621459.2019.1671197. The techniques used in the minimization portion of the procedure (the M-step) are derived from the procedures of the 'ncvreg' package (Breheny and Huang (2011) doi:10.1214/10-AOAS388) and 'grpreg' package (Breheny and Huang (2015) doi:10.1007/s11222-013-9424-2), with appropriate modifications to account for the estimation and penalization of the random effects. The 'ncvreg' and 'grpreg' packages also describe the MCP, SCAD, and LASSO penalties.

System requirements GNU make

Key Metrics

Version 1.5.4.4
R ≥ 3.6.0
Published 2024-01-18 71 days ago
Needs compilation? yes
License GPL-2
License GPL-3
CRAN checks glmmPen results

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Maintainer

Maintainer

Hillary Heiling

hmheiling@gmail.com

Authors

Hillary Heiling

aut / cre

Naim Rashid

aut

Quefeng Li

aut

Joseph Ibrahim

aut

Material

README
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

glmmPen archive

Depends

lme4
bigmemory
Rcpp ≥ 0.12.0
R ≥ 3.6.0

Imports

ggplot2
Matrix
methods
ncvreg
reshape2
rstan ≥ 2.18.1
stringr
mvtnorm
MASS
survival
rstantools
RcppParallel ≥5.0.1

Suggests

testthat
knitr
rmarkdown

LinkingTo

BH ≥ 1.66.0
bigmemory
Rcpp ≥ 0.12.0
RcppArmadillo
RcppEigen ≥ 0.3.3.3.0
rstan ≥ 2.18.1
StanHeaders ≥2.18.0
RcppParallel ≥ 5.0.1