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mcmcsae (version 0.7.9)

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

195

Version

0.7.9

License

GPL-3

Maintainer

Harm Jan Boonstra

Last Published

June 4th, 2025

Functions in mcmcsae (0.7.9)

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

Utility function to construct a sparse aggregation matrix from a factor

acceptance_rates

Return Metropolis-Hastings acceptance rates

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

anyNA,tabMatrix-method

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

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

create_TMVN_sampler

Set up a sampler object for sampling from a possibly truncated and degenerate multivariate normal distribution

Correlation factor structures in generic model components

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

create_block_cMVN_sampler

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

Specify a multinomial sampling distribution

f_gaussian_gamma

Specify a Gaussian-Gamma sampling distribution

Specify a Gamma sampling distribution

Specify a negative binomial sampling distribution

Specify a Poisson sampling distribution

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

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

maximize_log_lh_p

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

Specify a Gaussian sampling distribution

Get the parameter names from an mcdraws object

Fast matrix-vector multiplications

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

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

mcmcsae_example

Generate artificial data according to an additive spatio-temporal model

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

create_cMVN_sampler

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

Trace, density and autocorrelation plots

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

Create a sampler object

Specify a binomial sampling distribution

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

Create an object representing gamma prior distributions

poisson_control

Set computational options for the sampling algorithms

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

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

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

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

Create an object representing exponential prior distributions

posterior-moments

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

n_chains-n_draws-n_vars

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

Generate a data vector according to a model

Extract a list of parameter values for a single draw

mcmcsae-package

Markov Chain Monte Carlo Small Area Estimation

Create an object representing beta prior distributions

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

model-information-criteria

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

Compute possibly sparse model matrix

Create an object representing a possibly multivariate normal prior distribution

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

Set options for Metropolis-Hastings sampling

set_constraints

Set up a system of linear equality and/or inequality constraints

Create an object representing uniform prior distributions

predict.mcdraws

Generate draws from the predictive distribution

residuals-fitted-values

Extract draws of fitted values or residuals from an mcdraws object

Create an object representing truncated normal prior distributions

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

Summarise a draws component (dc) object

sampler_control

Set computational options for the sampling algorithms

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

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

summary.mcdraws

Summarise an mcdraws object

setup_CG_sampler

Set up conjugate gradient sampler

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

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

Read MCMC draws from a file

Set up a cluster for parallel computing

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

tabMatrix-indexing

S4 method for row and column subsetting a 'tabMatrix'

print.dc_summary

Display a summary of a dc object

print.mcdraws_summary

Print a summary of MCMC simulation results

Set computational options for the sampling algorithms

S4 methods for products of matrix objects

Set options for the conjugate gradient (CG) sampler

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

Run a Markov Chain Monte Carlo simulation

MCMC-object-conversion

Convert a draws component object to another format

Set up a GMRF structure for a generic model component

Simulation based calibration

Solve Ax=b by preconditioned conjugate gradients

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

MCMC-diagnostics

Compute MCMC diagnostic measures