Markov Chain Monte Carlo Small Area Estimation

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Documentation for package ‘mcmcsae’ version 0.7.6

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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
bart Create a model component object for a BART (Bayesian Additive Regression Trees) component in the linear predictor
CG_control Set options for the conjugate gradient (CG) sampler
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
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_gamma 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_log_lh_p Maximize the log-likelihood or log-posterior as defined by a sampler closure
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-TMVN-method Functions for specifying the method and corresponding options for sampling from a possibly truncated and degenerate multivariate normal distribution
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
m_direct Functions for specifying the method and corresponding options for sampling from a possibly truncated and degenerate multivariate normal distribution
m_Gibbs Functions for specifying the method and corresponding options for sampling from a possibly truncated and degenerate multivariate normal distribution
m_HMC Functions for specifying the method and corresponding options for sampling from a possibly truncated and degenerate multivariate normal distribution
m_HMCZigZag Functions for specifying the method and corresponding options for sampling from a possibly truncated and degenerate multivariate normal distribution
m_softTMVN Functions for specifying the method and corresponding options for sampling from a possibly truncated and degenerate multivariate normal distribution
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 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 Generalized Inverse Gaussian (GIG) prior distributions
pr_invchisq Create an object representing inverse chi-squared priors with possibly modeled degrees of freedom and scale parameters
pr_invwishart Create an object representing an inverse Wishart prior, possibly with modeled scale matrix
pr_MLiG Create an object representing a Multivariate Log inverse Gamma (MLiG) prior distribution
pr_normal Create an object representing a possibly multivariate normal prior distribution
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
sampler_control Set computational options for the sampling algorithms
setup_cluster Set up a cluster for parallel computing
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