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mcmcsae (version 0.6.0)

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

0.6.0

License

GPL-3

Maintainer

Harm Jan Boonstra

Last Published

January 20th, 2021

Functions in mcmcsae (0.6.0)

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

acceptance_rates

Return Metropolis-Hastings acceptance rates

Combine multiple draws objects to a single one by combining their draws

MCMC-object-conversion

Convert a draws component object to another format

Utility function to construct a sparse aggregation matrix from a factor

MCMC-diagnostics

Compute MCMC diagnostic measures

Combine multiple draws objects to a single one by combining their chains

Run a Markov Chain Monte Carlo simulation

anyNA,tabMatrix-method

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

Correlation structures

create_TMVN_sampler

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

nchains-ndraws-nvars

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

S4 methods for products of matrix objects

Generate a data vector according to a model

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

computeDesignMatrix

Compute list of design matrices for all terms in a model formula

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

compute_GMRF_matrices

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

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

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

Get the parameter names from a draws object

Create a sampler object

Functions for specifying a sampling distribution and link function

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

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

mcmcsae-package

MCMC Small Area Estimation

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

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

posterior-moments

Get means or standard deviations of parameters from the MCMC output in a draws object

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 regression component in the variance function of a gaussian sampling distribution

Trace, density and autocorrelation plots

residuals-fitted-values

Extract draws of fitted values or residuals from a draws object

Fast matrix-vector multiplications

Maximize log-likelihood defined inside a sampler function

Create an object containing information about exponential prior distributions

Extract weights from a draws object

Generate draws from the predictive distribution

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

Select a subset of chains, samples and parameters from a draws component

Set global options relating to computational details

Summarize a draws object

Read MCMC draws from a file

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

tabMatrix-indexing

S4 method for row and column subsetting a 'tabMatrix'

print.dc_summary

Display a summary of a dc object

Summarize a draws component object

print.draws_summary

Print a summary of MCMC simulation results

Set up a cluster for parallel computing

Transform one or more draws components 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