MSPE                    Compute normalized mean squared prediction
                        error based on accuracy to impute missing data
                        values
cluster_plot            Plot estimated functions for experimental units
                        faceted by cluster versus data to assess fit.
cps                     Monthly employment counts from 1990 - 2013 from
                        the Current Population Survey
fit_compare             Side-by-side plot panels that compare latent
                        function values to data for different
                        estimation models
gen_informative_sample
                        Generate a finite population and take an
                        informative single or two-stage sample.
gmrfdpPost              Run a Bayesian functional data model under an
                        instrinsic GMRF prior whose precision
                        parameters employ a DP prior
gmrfdpcountPost         Run a Bayesian functional data model under an
                        instrinsic GMRF prior whose precision
                        parameters employ a DP prior for a COUNT data
                        response type where: y ~ poisson(E*exp(Psi))
                        Psi ~ N(gamma,tau_e^-1) which is a
                        Poisson-lognormal model
gmrfdpgrow              Bayesian instrinsic Gaussian Markov Random
                        Field model for dependent time-indexed
                        functions
gpBFixPost              Run a Bayesian functional data model under a GP
                        prior with a fixed clustering structure that
                        co-samples latent functions, 'bb_i'.
gpFixPost               Run a Bayesian functional data model under a GP
                        prior whose parameters employ a DP prior
gpPost                  Run a Bayesian functional data model under a GP
                        prior whose parameters employ a DP prior
gpdpPost                Run a Bayesian functional data model under a GP
                        prior whose parameters employ a DP prior
gpdpbPost               Run a Bayesian functional data model under a GP
                        prior whose parameters employ a DP prior
gpdpgrow                Bayesian non-parametric dependent Gaussian
                        process model for time-indexed functional data
growfunctions-package   Bayesian Non-Parametric Models for Estimating a
                        Set of Denoised, Latent Functions From an
                        Observed Collection of Domain-Indexed
                        Time-Series
informative_plot        Plot credible intervals for parameters to
                        compare ignoring with weighting an informative
                        sample
plot_cluster            Plot estimated functions, faceted by cluster
                        numbers, for a known clustering
predict_functions       Use the model-estimated covariance parameters
                        from gpdpgrow() or gmrdpgrow to predict the
                        function at future time points.
predict_functions.gmrfdpgrow
                        Use the model-estimated iGMRF precision
                        parameters from gmrfdpgrow() to predict the
                        iGMRF function at future time points.  Inputs
                        the 'gmrfdpgrow' object of estimated
                        parameters.
predict_functions.gpdpgrow
                        Use the model-estimated GP covariance
                        parameters from gpdpgrow() to predict the GP
                        function at future time points.  Inputs the
                        'gpdpgrow' object of estimated parameters.
predict_plot            Plot estimated functions both at estimated and
                        predicted time points with 95% credible
                        intervals.
samples                 Produce samples of MCMC output
samples.gmrfdpgrow      Produce samples of MCMC output
