netcmc: Spatio-Network Generalised Linear Mixed Models for Areal Unit and Network Data

Implements a class of univariate and multivariate spatio-network generalised linear mixed models for areal unit and network data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson. Spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution following the Leroux model (Leroux et al. (2000) <doi:10.1007/978-1-4612-1284-3_4>). Network structures are modelled by a set of random effects that reflect a multiple membership structure (Browne et al. (2001) <doi:10.1177/1471082X0100100202>).

Version:	1.0.2
Depends:	R (≥ 4.0.0), MCMCpack
Imports:	Rcpp (≥ 1.0.4), coda, ggplot2, mvtnorm, MASS
LinkingTo:	Rcpp, RcppArmadillo, RcppProgress
Suggests:	testthat, igraph, magic
Published:	2022-11-08
DOI:	10.32614/CRAN.package.netcmc
Author:	George Gerogiannis, Mark Tranmer, Duncan Lee
Maintainer:	George Gerogiannis <g.gerogiannis.1 at research.gla.ac.uk>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
CRAN checks:	netcmc results

Documentation:

Reference manual:

netcmc.pdf

Downloads:

Package source:	netcmc_1.0.2.tar.gz
Windows binaries:	r-devel: netcmc_1.0.2.zip, r-release: netcmc_1.0.2.zip, r-oldrel: netcmc_1.0.2.zip
macOS binaries:	r-release (arm64): netcmc_1.0.2.tgz, r-oldrel (arm64): netcmc_1.0.2.tgz, r-release (x86_64): netcmc_1.0.2.tgz, r-oldrel (x86_64): netcmc_1.0.2.tgz
Old sources:	netcmc archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=netcmc to link to this page.