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mcmcsae (version 0.7.8)

Markov Chain Monte Carlo Small Area Estimation

Description

Fit multi-level models with possibly correlated random effects using Markov Chain Monte Carlo simulation. Such models allow smoothing over space and time and are useful in, for example, small area estimation.

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Version

Install

install.packages('mcmcsae')

Monthly Downloads

238

Version

0.7.8

License

GPL-3

Maintainer

Harm Jan Boonstra

Last Published

December 11th, 2024

Functions in mcmcsae (0.7.8)

Utility function to construct a sparse aggregation matrix from a factor

anyNA,tabMatrix-method

S4 method for generic 'anyNA' and signature 'tabMatrix'

Set options for the conjugate gradient (CG) sampler

Fast matrix-vector multiplications

Create a model component object for a BART (Bayesian Additive Regression Trees) component in the linear predictor

Get and set the variable labels of a draws component object for a vector-valued parameter

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_block_cMVN_sampler

Set up a a function for direct sampling from a constrained multivariate normal distribution

Correlation factor structures in generic model components

Create a model component object for a regression (fixed effects) component in the linear predictor with measurement errors in quantitative covariates

mcmcsae_example

Generate artificial data according to an additive spatio-temporal model

Plot a set of model coefficients or predictions with uncertainty intervals based on summaries of simulation results or other objects.

Create a model component object for a generic random effects component in the linear predictor

Create a sampler object

Functions for specifying a sampling distribution and link function

mcmcsae-package

Markov Chain Monte Carlo Small Area Estimation

Create a model object for group-level regression effects within a generic random effects component.

Combine multiple mcdraws objects into a single one by combining their chains

Extract a list of parameter values for a single draw

model-information-criteria

Compute DIC, WAIC and leave-one-out cross-validation model measures

Compute possibly sparse model matrix

posterior-moments

Get means or standard deviations of parameters from the MCMC output in an mcdraws object

Create an object representing a Multivariate Log inverse Gamma (MLiG) prior distribution

Create an object representing gamma prior distributions

Create an object representing beta prior distributions

Create an object representing a possibly multivariate normal prior distribution

Generate a data vector according to a model

Set computational options for the sampling algorithms used for a 'gen' model component

Create an object representing truncated normal prior distributions

Set options for Metropolis-Hastings sampling

setup_CG_sampler

Set up conjugate gradient sampler

Create an object representing Generalized Inverse Gaussian (GIG) prior distributions

Create a model component object for a variance factor component in the variance function of a gaussian sampling distribution

Create a model component object for a regression component in the variance function of a gaussian sampling distribution

Create an object representing uniform prior distributions

predict.mcdraws

Generate draws from the predictive distribution

Summarize a draws component (dc) object

print.mcdraws_summary

Print a summary of MCMC simulation results

print.dc_summary

Display a summary of a dc object

Select a subset of chains, samples and parameters from a draws component (dc) object

summary.mcdraws

Summarize an mcdraws object

weights.mcdraws

Extract weights from an mcdraws object

Create a model component object for a regression (fixed effects) component in the linear predictor

Read MCMC draws from a file

Trace, density and autocorrelation plots for (parameters of a) draws component (dc) object

Trace, density and autocorrelation plots

maximize_log_lh_p

Maximize the log-likelihood or log-posterior as defined by a sampler closure

Create a model component object for an offset, i.e. fixed, non-parametrized term in the linear predictor

Set up a cluster for parallel computing

Compute a Monte Carlo estimate of the marginal variances of a (I)GMRF

sampler_control

Set computational options for the sampling algorithms

residuals-fitted-values

Extract draws of fitted values or residuals from an mcdraws object

Get the parameter names from an mcdraws object

Create an object representing exponential prior distributions

n_chains-n_draws-n_vars

Get the number of chains, samples per chain or the number of variables in a simulation object

Create an object representing a degenerate prior fixing a parameter (vector) to a fixed value

Transform one or more draws component objects into a new one by applying a function

Create an object representing an inverse Wishart prior, possibly with modeled scale matrix

Create an object representing inverse chi-squared priors with possibly modeled degrees of freedom and scale parameters

tabMatrix-indexing

S4 method for row and column subsetting a 'tabMatrix'

Compute autocovariance or autocorrelation function via Wiener-Khinchin theorem using Fast Fourier Transform

Simulation based calibration

Run a Markov Chain Monte Carlo simulation

Functions for specifying the method and corresponding options for sampling from a possibly truncated and degenerate multivariate normal distribution

MCMC-diagnostics

Compute MCMC diagnostic measures

Set a GMRF structure for a generic model component

MCMC-object-conversion

Convert a draws component object to another format

Solve Ax=b by preconditioned conjugate gradients

S4 methods for products of matrix objects

acceptance_rates

Return Metropolis-Hastings acceptance rates

Combine multiple mcdraws objects into a single one by combining their draws

Set options for Cholesky decomposition

computeDesignMatrix

Compute a list of design matrices for all terms in a model formula, or based on a sampler environment