# Markov Chain Monte Carlo Small Area Estimation

## Documentation for package ‘mcmcsae’ version 0.7.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

mcmcsae-package | Markov Chain Monte Carlo Small Area Estimation
%m*v% | Fast matrix-vector multiplications
acceptance_rates | Return Metropolis-Hastings acceptance rates
aggrMatrix | Utility function to construct a sparse aggregation matrix from a factor
as.array.dc | Convert a draws component object to another format
as.matrix.dc | Convert a draws component object to another format
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
compute_DIC | Compute DIC, WAIC and leave-one-out cross-validation model measures
compute_GMRF_matrices | Compute (I)GMRF incidence, precision and restriction matrices corresponding to a generic model component
compute_WAIC | Compute DIC, WAIC and leave-one-out cross-validation model measures
correlation | Correlation structures
create_sampler | Create a sampler object
create_TMVN_sampler | Set up a sampler object for sampling from a possibly truncated and degenerate multivariate normal distribution
crossprod_mv | Fast matrix-vector multiplications
fitted.mcdraws | Extract draws of fitted values or residuals from an mcdraws object
f_binomial | Functions for specifying a sampling distribution and link function
f_gaussian | Functions for specifying a sampling distribution and link function
f_multinomial | Functions for specifying a sampling distribution and link function
f_negbinomial | Functions for specifying a sampling distribution and link function
f_poisson | Functions for specifying a sampling distribution and link function
gen | Create a model component object for a generic random effects component in the linear predictor
generate_data | Generate a data vector according to a model
get_draw | Extract a list of parameter values for a single draw
get_means | Get means or standard deviations of parameters from the MCMC output in an mcdraws object
get_sds | Get means or standard deviations of parameters from the MCMC output in an mcdraws object
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
labels.dc | Get and set the variable labels of a draws component object for a vector-valued parameter
labels<- | Get and set the variable labels of a draws component object for a vector-valued parameter
loo.mcdraws | Compute DIC, WAIC and leave-one-out cross-validation model measures
matrix-vector | Fast matrix-vector multiplications
maximize_llh | Maximize log-likelihood defined inside a sampler function
MCMC-diagnostics | Compute MCMC diagnostic measures
MCMC-object-conversion | Convert a draws component object to another format
mcmcsae | Markov Chain Monte Carlo Small Area Estimation
mcmcsae-family | Functions for specifying a sampling distribution and link function
mcmcsae_example | Generate artificial data according to an additive spatio-temporal model
MCMCsim | Run a Markov Chain Monte Carlo simulation
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
nchains | Get the number of chains, samples per chain or the number of variables in a simulation object
nchains-ndraws-nvars | Get the number of chains, samples per chain or the number of variables in a simulation object
ndraws | Get the number of chains, samples per chain or the number of variables in a simulation object
nvars | Get the number of chains, samples per chain or the number of variables in a simulation object
n_eff | Compute MCMC diagnostic measures
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.
posterior-moments | Get means or standard deviations of parameters from the MCMC output in an mcdraws object
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
pr_exp | Create an object containing information about exponential prior distributions
pr_fixed | Create an object containing information about a degenerate prior fixing a parameter to a fixed value
pr_gig | Create an object containing information about Generalized Inverse Gaussian (GIG) prior distributions
pr_invchisq | Create an object containing information about inverse chi-squared priors with possibly modeled degrees of freedom and scale parameters
pr_invwishart | Create an object containing information about an inverse Wishart prior, possibly with modeled scale matrix
read_draws | Read MCMC draws from a file
reg | Create a model component object for a regression (fixed effects) component in the linear predictor
residuals-fitted-values | Extract draws of fitted values or residuals from an mcdraws object
residuals.mcdraws | Extract draws of fitted values or residuals from an mcdraws object
R_hat | Compute MCMC diagnostic measures
setup_cluster | Set up a cluster for parallel computing
set_opts | Set global options relating to computational details
stop_cluster | Stop a cluster
subset.dc | Select a subset of chains, samples and parameters from a draws component (dc) object
summary.dc | Summarize a draws component (dc) object
summary.mcdraws | Summarize an mcdraws object
to_draws_array | Convert a draws component object to another format
to_mcmc | Convert a draws component object to another format
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
waic.mcdraws | Compute DIC, WAIC and leave-one-out cross-validation model measures
weights.mcdraws | Extract weights from an mcdraws object