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mcmcsae (version 0.7.4)

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

0.7.4

License

GPL-3

Maintainer

Harm Jan Boonstra

Last Published

June 29th, 2023

Functions in mcmcsae (0.7.4)

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

computeDesignMatrix

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

create_TMVN_sampler

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

Create a sampler object

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

compute_GMRF_matrices

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

Correlation structures

anyNA,tabMatrix-method

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

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

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

Extract a list of parameter values for a single draw

Maximize log-likelihood defined inside a sampler function

Fast matrix-vector multiplications

mcmcsae_example

Generate artificial data according to an additive spatio-temporal model

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

mcmcsae-TMVN-method

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

mcmcsae-package

Markov Chain Monte Carlo Small Area Estimation

Generate a data vector according to a model

Functions for specifying a sampling distribution and link function

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

Create an object containing information about exponential prior distributions

nchains-ndraws-nvars

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

Get the parameter names from an mcdraws object

model-information-criteria

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

Compute possibly sparse model matrix

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

posterior-moments

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

Trace, density and autocorrelation plots

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

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

Create an object containing information about a degenerate prior fixing a parameter to a fixed value

Read MCMC draws from a file

predict.mcdraws

Generate draws from the predictive distribution

Create an object containing information about an inverse Wishart prior, possibly with modeled scale matrix

residuals-fitted-values

Extract draws of fitted values or residuals from an mcdraws object

Create an object containing information about Generalized Inverse Gaussian (GIG) prior distributions

print.mcdraws_summary

Print a summary of MCMC simulation results

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

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

print.dc_summary

Display a summary of a dc object

Set up a cluster for parallel computing

setup_CG_sampler

Set up conjugate gradient sampler

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

tabMatrix-indexing

S4 method for row and column subsetting a 'tabMatrix'

summary.mcdraws

Summarize an mcdraws object

weights.mcdraws

Extract weights from an mcdraws object

Summarize a draws component (dc) 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 variance factor component in the variance function of a gaussian sampling distribution

Set global options relating to computational details

acceptance_rates

Return Metropolis-Hastings acceptance rates

S4 methods for products of matrix objects

Run a Markov Chain Monte Carlo simulation

Set options for the conjugate gradient (CG) sampler

Utility function to construct a sparse aggregation matrix from a factor

MCMC-object-conversion

Convert a draws component object to another format

MCMC-diagnostics

Compute MCMC diagnostic measures

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

Simulation based calibration

Solve Ax=b by preconditioned conjugate gradients