LambdaSeq               Calculation of Penalty Parameter Sequence
                        (Lambda Sequence)
adaptControl            Control of Metropolis-within-Gibbs Adaptive
                        Random Walk Sampling Procedure Controls the
                        adaptive random walk Metropolis-within-Gibbs
                        sampling procedure.
basal                   Basal dataset: A composition of cancer datasets
                        with top scoring pairs (TSPs) as covariates and
                        binary response indicating if the subject's
                        cancer subtype was basal-like. A dataset
                        composed of four datasets combined from studies
                        that contain gene expression data from subjects
                        with several types of cancer. Two of these
                        datasets contain gene expression data for
                        subjects with Pancreatic Ductal Adenocarcinoma
                        (PDAC), one dataset contains data for subjects
                        with Breast Cancer, and the fourth dataset
                        contains data for subjects with Bladder Cancer.
                        The response of interest is whether or not the
                        subject's cancer subtype was the basal-like
                        subtype. See articles Rashid et al. (2020)
                        "Modeling Between-Study Heterogeneity for
                        Improved Replicability in Gene Signature
                        Selection and Clinical Prediction" and Moffitt
                        et al. (2015) "Virtual microdissection
                        identifies distinct tumor- and stroma-specific
                        subtypes of pancreatic ductal adenocarcinoma"
                        for further details on these four datasets.
glmm                    Fit a Generalized Mixed Model via Monte Carlo
                        Expectation Conditional Minimization (MCECM)
glmmPen                 Fit Penalized Generalized Mixed Models via
                        Monte Carlo Expectation Conditional
                        Minimization (MCECM)
glmmPen_FA              Fit Penalized Generalized Mixed Models via
                        Monte Carlo Expectation Conditional
                        Minimization (MCECM)
glmm_FA                 Fit a Generalized Mixed Model via Monte Carlo
                        Expectation Conditional Minimization (MCECM)
lambdaControl           Control of Penalization Parameters and
                        Selection Criteria
optimControl            Control of Penalized Generalized Linear Mixed
                        Model Fitting
pglmmObj-class          Class 'pglmmObj' of Fitted Penalized
                        Generalized Mixed-Effects Models for package
                        'glmmPen'
phmm                    Fit a Proportional Hazards Mixed Model via
                        Monte Carlo Expectation Conditional
                        Minimization (MCECM) using a Piecewise Constant
                        Hazard Mixed Model Approximation
phmmPen                 Fit Penalized Proportional Hazards Mixed Models
                        via Monte Carlo Expectation Conditional
                        Minimization (MCECM) using a Piecewise Constant
                        Hazard Mixed Model Approximation
phmmPen_FA              Fit a Penalized Proportional Hazards Mixed
                        Model via Monte Carlo Expectation Conditional
                        Minimization (MCECM) using a Piecewise Constant
                        Hazard Mixed Model Approximation
phmm_FA                 Fit a Proportional Hazards Mixed Model via
                        Monte Carlo Expectation Conditional
                        Minimization (MCECM) using a Piecewise Constant
                        Hazard Mixed Model Approximation
plot_mcmc               Plot Diagnostics for MCMC Posterior Draws of
                        the Random Effects
rControl                Control of Latent Factor Model Number
                        Estimation Constructs the control structure for
                        the estimation of the number of latent factors
                        (r) for use within the 'glmmPen_FA' and
                        'glmm_FA' estimation procedures.
sim.data                Simulates data to use for the 'glmmPen' package
survivalControl         Control for Fitting Piecewise Constant Hazard
                        Mixed Models as an Approximation to Fitting Cox
                        Proportional Hazards Mixed Models
survival_data           Convert Input Survival Data Into Long-Form Data
                        Needed for Fitting a Piecewise Exponential
                        Model
