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.
fit_dat                 Fit a Penalized Generalized Mixed Model via
                        Monte Carlo Expectation Conditional
                        Minimization (MCECM) 'fit_dat' is used to fit a
                        penalized generalized mixed model via Monte
                        Carlo Expectation Conditional Minimization
                        (MCECM) for a single tuning parameter
                        combinations and is called within 'glmmPen' or
                        'glmm' (cannot be called directly by user)
glFormula_edit          Extracting Useful Vectors and Matrices from
                        Formula and Data Information
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_FineSearch      Fit a Penalized Generalized Mixed Model via
                        Monte Carlo Expectation Conditional
                        Minimization (MCECM) using a finer penalty grid
                        search 'glmmPen_FineSearch' finds the best
                        model from the selection results of a
                        'pglmmObj' object created by 'glmmPen',
                        identifies a more targeted grid search around
                        the optimum lambda penalty values, and performs
                        model selection on this finer grid search.
lambdaControl           Control of Penalization Parameters and
                        Selection Criteria
optimControl            Control of Penalized Generalized Linear Mixed
                        Model Fitting Constructs the control structure
                        for the optimization of the penalized mixed
                        model fit algorithm.
pglmmObj-class          Class 'pglmmObj' of Fitted Penalized
                        Generalized Mixed-Effects Models for package
                        'glmmPen'
plot_mcmc               Plot Diagnostics for MCMC Posterior Draws of
                        the Random Effects
select_tune             Fit a Sequence of Penalized Generalized Mixed
                        Model via Monte Carlo Expectation Conditional
                        Minimization (MCECM) 'select_tune' is used to
                        fit a sequence of penalized generalized mixed
                        models via Monte Carlo Expectation Conditional
                        Minimization (MCECM) for multiple tuning
                        parameter combinations and is called within
                        'glmmPen' (cannot be called directly by user)
sim.data                Simulates data to use for the 'glmmPen' package
                        Simulates data to use for testing the 'glmmPen'
                        package. Possible parameters to specify
                        includes number of total covariates, number of
                        non-zero fixed and random effects, and the
                        magnitude of the random effect covariance
                        values.
