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mcmcsae (version 0.5.0)

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

0.5.0

License

GPL-3

Maintainer

Harm Jan Boonstra

Last Published

September 1st, 2020

Functions in mcmcsae (0.5.0)

acceptance_rates

Return Metropolis-Hastings acceptance rates

S4 methods for products of matrix objects

Run a Markov Chain Monte Carlo simulation

MCMC-diagnostics

Compute MCMC diagnostic measures

MCMC-object-conversion

Convert a draws component object to another format

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

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

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

Utility function to construct a sparse aggregation matrix from a factor

Generate a data vector according to a model

Extract a list of parameter values for a single draw

anyNA,tabMatrix-method

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

compute_GMRF_matrices

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

nchains-ndraws-nvars

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

Compute possibly sparse model matrix

computeDesignMatrix

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

Correlation structures

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

tabMatrix-indexing

S4 method for row and column subsetting a 'tabMatrix'

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

create_TMVN_sampler

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

Transform one or more draws components into a new one by applying a function

Summarize a draws component object

mcmcsae_example

Generate artificial data according to an additive spatio-temporal model

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

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

Create a sampler object

Fast matrix-vector multiplications

Generate draws from the predictive distribution

Maximize log-likelihood defined inside a sampler function

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

model-information-criteria

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

mcmcsae-package

MCMC Small Area Estimation

Functions for specifying a sampling distribution and link function

print.draws_summary

Print a summary of MCMC simulation results

Read MCMC draws from a file

Set global options relating to computational details

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

print.dc_summary

Display a summary of a dc object

Get the parameter names from a draws object

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

Set up a cluster for parallel computing

posterior-moments

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

residuals-fitted-values

Extract draws of fitted values or residuals from a draws object

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

Trace, density and autocorrelation plots

Summarize a draws object

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

Create an object containing information about exponential prior distributions

Extract weights from a draws object

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