Learn R Programming

⚠️There's a newer version (0.7.8) of this package.Take me there.

mcmcsae (version 0.7.6)

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.

Copy Link

Version

Install

install.packages('mcmcsae')

Monthly Downloads

238

Version

0.7.6

License

GPL-3

Maintainer

Harm Jan Boonstra

Last Published

December 2nd, 2023

Functions in mcmcsae (0.7.6)

Run a Markov Chain Monte Carlo simulation

Set options for the conjugate gradient (CG) sampler

compute_GMRF_matrices

Compute (I)GMRF incidence, precision and restriction matrices corresponding to a generic model component

Create a sampler object

create_TMVN_sampler

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

Set options for Cholesky decomposition

Correlation structures

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

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

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

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

computeDesignMatrix

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

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

Extract a list of parameter values for a single draw

maximize_log_lh_p

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

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.

Fast matrix-vector multiplications

nchains-ndraws-nvars

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

mcmcsae-TMVN-method

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

Compute possibly sparse model matrix

model-information-criteria

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

Get the parameter names from an mcdraws object

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

Create an object representing gamma prior distributions

posterior-moments

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

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

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

Create an object representing a possibly multivariate normal prior distribution

Set up a cluster for parallel computing

Create an object representing exponential prior distributions

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

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

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

Trace, density and autocorrelation plots

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

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

summary.mcdraws

Summarize an mcdraws object

print.mcdraws_summary

Print a summary of MCMC simulation results

Read MCMC draws from a file

tabMatrix-indexing

S4 method for row and column subsetting a 'tabMatrix'

weights.mcdraws

Extract weights from an mcdraws object

sampler_control

Set computational options for the sampling algorithms

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

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

predict.mcdraws

Generate draws from the predictive distribution

print.dc_summary

Display a summary of a dc object

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

Summarize a draws component (dc) object

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

setup_CG_sampler

Set up conjugate gradient sampler

residuals-fitted-values

Extract draws of fitted values or residuals from an mcdraws object

acceptance_rates

Return Metropolis-Hastings acceptance rates

Solve Ax=b by preconditioned conjugate gradients

MCMC-diagnostics

Compute MCMC diagnostic measures

MCMC-object-conversion

Convert a draws component object to another format

Utility function to construct a sparse aggregation matrix from a factor

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

S4 methods for products of matrix objects

Simulation based calibration