CG_control              Set options for the conjugate gradient (CG)
                        sampler
GMRF_structure          Set up a GMRF structure for a generic model
                        component
MCMC-diagnostics        Compute MCMC diagnostic measures
MCMC-object-conversion
                        Convert a draws component object to another
                        format
MCMCsim                 Run a Markov Chain Monte Carlo simulation
SBC_test                Simulation based calibration
TMVN-methods            Functions for specifying the method and
                        corresponding options for sampling from a
                        possibly truncated and degenerate multivariate
                        normal distribution
acceptance_rates        Return Metropolis-Hastings acceptance rates
aggrMatrix              Utility function to construct a sparse
                        aggregation matrix from a factor
brt                     Create a model component object for a BART
                        (Bayesian Additive Regression Trees) component
                        in the linear predictor
chol_control            Set options for Cholesky decomposition
combine_chains          Combine multiple mcdraws objects into a single
                        one by combining their chains
combine_iters           Combine multiple mcdraws objects into a single
                        one by combining their draws
computeDesignMatrix     Compute a list of design matrices for all terms
                        in a model formula, or based on a sampler
                        environment
correlation             Correlation factor structures in generic model
                        components
create_TMVN_sampler     Set up a sampler object for sampling from a
                        possibly truncated and degenerate multivariate
                        normal distribution
create_cMVN_sampler     Set up a function for direct sampling from a
                        constrained multivariate normal distribution
create_sampler          Create a sampler object
f_binomial              Specify a binomial sampling distribution
f_gamma                 Specify a Gamma sampling distribution
f_gaussian              Specify a Gaussian sampling distribution
f_gaussian_gamma        Specify a Gaussian-Gamma sampling distribution
f_multinomial           Specify a multinomial sampling distribution
f_negbinomial           Specify a negative binomial sampling
                        distribution
f_poisson               Specify a Poisson sampling distribution
gen                     Create a model component object for a generic
                        random effects component in the linear
                        predictor
gen_control             Set computational options for the sampling
                        algorithms used for a 'gen' model component
generate_data           Generate a data vector according to a model
get_draw                Extract a list of parameter values for a single
                        draw
glreg                   Create a model object for group-level
                        regression effects within a generic random
                        effects component.
labels                  Get and set the variable labels of a draws
                        component object for a vector-valued parameter
matrix-vector           Fast matrix-vector multiplications
maximize_log_lh_p       Maximise the log-likelihood or log-posterior as
                        defined by a sampler closure
mc_offset               Create a model component object for an offset,
                        i.e. fixed, non-parametrised term in the linear
                        predictor
mcmcsae-package         Markov Chain Monte Carlo Small Area Estimation
mcmcsae_example         Generate artificial data according to an
                        additive spatio-temporal model
mec                     Create a model component object for a
                        regression (fixed effects) component in the
                        linear predictor with measurement errors in
                        quantitative covariates
model-information-criteria
                        Compute DIC, WAIC and leave-one-out
                        cross-validation model measures
model_matrix            Compute possibly sparse model matrix
n_chains-n_draws-n_vars
                        Get the number of chains, samples per chain or
                        the number of variables in a simulation object
negbin_control          Set computational options for the sampling
                        algorithms
par_names               Get the parameter names from an mcdraws object
plot.dc                 Trace, density and autocorrelation plots for
                        (parameters of a) draws component (dc) object
plot.mcdraws            Trace, density and autocorrelation plots
plot_coef               Plot a set of model coefficients or predictions
                        with uncertainty intervals based on summaries
                        of simulation results or other objects.
poisson_control         Set computational options for the sampling
                        algorithms
posterior-moments       Get means or standard deviations of parameters
                        from the MCMC output in an mcdraws object
pr_MLiG                 Create an object representing a Multivariate
                        Log inverse Gamma (MLiG) prior distribution
pr_beta                 Create an object representing beta prior
                        distributions
pr_exp                  Create an object representing exponential prior
                        distributions
pr_fixed                Create an object representing a degenerate
                        prior fixing a parameter (vector) to a fixed
                        value
pr_gamma                Create an object representing gamma prior
                        distributions
pr_gig                  Create an object representing Generalised
                        Inverse Gaussian (GIG) prior distributions
pr_invchisq             Create an object representing inverse
                        chi-squared priors with possibly modelled
                        degrees of freedom and scale parameters
pr_invwishart           Create an object representing an inverse
                        Wishart prior, possibly with modelled scale
                        matrix
pr_normal               Create an object representing a possibly
                        multivariate normal prior distribution
pr_truncnormal          Create an object representing truncated normal
                        prior distributions
pr_unif                 Create an object representing uniform prior
                        distributions
predict.mcdraws         Generate draws from the predictive distribution
print.dc_summary        Display a summary of a 'dc' object
print.mcdraws_summary   Print a summary of MCMC simulation results
read_draws              Read MCMC draws from a file
reg                     Specify a regression (fixed effects) component
                        in the linear predictor
residuals-fitted-values
                        Extract draws of fitted values or residuals
                        from an mcdraws object
s                       Specify a smooth term component of the linear
                        predictor
sampler_control         Set computational options for the sampling
                        algorithms
set_MH                  Set options for Metropolis-Hastings sampling
set_constraints         Set up a system of linear equality and/or
                        inequality constraints
setup_cluster           Set up a cluster for parallel computing
sim_marg_var            Compute a Monte Carlo estimate of the marginal
                        variances of a (I)GMRF
stop_cluster            Stop a cluster
subset.dc               Select a subset of chains, samples and
                        parameters from a draws component (dc) object
summary.dc              Summarise a draws component (dc) object
summary.mcdraws         Summarise an mcdraws object
transform_dc            Transform one or more draws component objects
                        into a new one by applying a function
vfac                    Create a model component object for a variance
                        factor component in the variance function of a
                        gaussian sampling distribution
vreg                    Create a model component object for a
                        regression component in the variance function
                        of a gaussian sampling distribution
weights.mcdraws         Extract weights from an mcdraws object
