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mcmcsae (version 0.7.7)

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

0.7.7

License

GPL-3

Maintainer

Harm Jan Boonstra

Last Published

February 27th, 2024

Functions in mcmcsae (0.7.7)

create_TMVN_sampler

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

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

create_cMVN_sampler

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

Fast matrix-vector multiplications

Generate a data vector according to a model

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

Extract a list of parameter values for a single draw

Create a sampler object

maximize_log_lh_p

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

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

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

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

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

model-information-criteria

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

Functions for specifying a sampling distribution and link function

Compute possibly sparse model matrix

Trace, density and autocorrelation plots

Create an object representing gamma prior distributions

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

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

posterior-moments

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

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

mcmcsae_example

Generate artificial data according to an additive spatio-temporal model

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

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

Create an object representing a possibly multivariate normal prior distribution

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

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

print.mcdraws_summary

Print a summary of MCMC simulation results

Read MCMC draws from a file

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

Create an object representing exponential prior distributions

print.dc_summary

Display a summary of a dc object

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

predict.mcdraws

Generate draws from the predictive distribution

mcmcsae-package

Markov Chain Monte Carlo Small Area Estimation

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

Set up a cluster for parallel computing

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

weights.mcdraws

Extract weights from an mcdraws object

sampler_control

Set computational options for the sampling algorithms

summary.mcdraws

Summarize an mcdraws object

tabMatrix-indexing

S4 method for row and column subsetting a 'tabMatrix'

setup_CG_sampler

Set up conjugate gradient sampler

Summarize a draws component (dc) object

residuals-fitted-values

Extract draws of fitted values or residuals from an mcdraws object

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

Simulation based calibration

MCMC-diagnostics

Compute MCMC diagnostic measures

MCMC-object-conversion

Convert a draws component object to another format

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

acceptance_rates

Return Metropolis-Hastings acceptance rates

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

Set options for the conjugate gradient (CG) sampler

Run a Markov Chain Monte Carlo simulation

computeDesignMatrix

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

Utility function to construct a sparse aggregation matrix from a factor

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 chains

Solve Ax=b by preconditioned conjugate gradients

S4 methods for products of matrix objects

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

Set options for Cholesky decomposition

Correlation factor structures in generic model components