High Dimensional Penalized Generalized Linear Mixed Models (pGLMM)
Control of Metropolis-within-Gibbs Adaptive Random Walk Sampling Proce...
Fit a Generalized Mixed Model via Monte Carlo Expectation Conditional ...
Fit a Generalized Mixed Model via Monte Carlo Expectation Conditional ...
Fit Penalized Generalized Mixed Models via Monte Carlo Expectation Con...
Fit Penalized Generalized Mixed Models via Monte Carlo Expectation Con...
Control of Penalization Parameters and Selection Criteria
Calculation of Penalty Parameter Sequence (Lambda Sequence)
Control of Penalized Generalized Linear Mixed Model Fitting
Class pglmmObj of Fitted Penalized Generalized Mixed-Effects Models ...
Fit a Proportional Hazards Mixed Model via Monte Carlo Expectation Con...
Fit a Proportional Hazards Mixed Model via Monte Carlo Expectation Con...
Fit a Penalized Proportional Hazards Mixed Model via Monte Carlo Expec...
Fit Penalized Proportional Hazards Mixed Models via Monte Carlo Expect...
Plot Diagnostics for MCMC Posterior Draws of the Random Effects
Control of Latent Factor Model Number EstimationConstructs the control...
Simulates data to use for the glmmPen package
Convert Input Survival Data Into Long-Form Data Needed for Fitting a P...
Control for Fitting Piecewise Constant Hazard Mixed Models as an Appro...
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.