stats, minqa, Matrix, Rcpp (>= 0.12.12), methods, GMMBoost

A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided,
see Groll and Tutz (2014) <doi:10.1007/s11222-012-9359-z>.
See also Groll and Tutz (2017) <doi:10.1007/s10985-016-9359-y> for discrete survival models including heterogeneity.

Generalized linear models with random intercept
A Maximum Likelihood approach to mixed models.

A Maximum Likelihood and bootstrap approach to mixed models.

Generalized linear models with clustering

A Maximum Likelihood and bootstrap approach to mixed models

A Maximum Likelihood and bootstrap approach to mixed
models

Binary and Poisson regression for clustered data, fixed
and random effects with bootstrapping.

Generalized Linear Models with Clustering
Binomial and Poisson regression for clustered data, fixed
and random effects with bootstrapping.

c(person("Göran", "Broström", role = c("aut", "cre"),
email = "goran.brostrom@umu.se"),
person("Jianming", "Jin", role = "ctb"),
person("Henrik", "Holmberg", role = "ctb"))
Göran Broström [aut, cre],
Jianming Jin [ctb],
Henrik Holmberg [ctb]

High Dimensional Penalized Generalized Linear Mixed Models
(pGLMM)
c(
person("Hillary", "Heiling", email = "hheiling@live.unc.edu", role = c("aut", "cre")),
person("Naim", "Rashid", email = "naim@unc.edu", role = c("aut")),
person("Quefeng", "Li", email = "quefeng@email.unc.edu", role = c("aut")))
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.
ggplot2, Matrix, methods, ncvreg, reshape2, rstan (>= 2.18.1),
rstantools (>= 2.0.0), stringr, mvtnorm, MASS
Hillary Heiling [aut, cre],
Naim Rashid [aut],
Quefeng Li [aut]