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: 1.0.1
Depends: R (≥ 4.0.0), MCMCpack
Imports: Rcpp (≥ 1.0.4), coda, ggplot2, mvtnorm, MASS
LinkingTo: Rcpp, RcppArmadillo, RcppProgress
Suggests: testthat, igraph, magic
Published: 2022-06-29
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_1.0.1.tar.gz
Windows binaries: r-devel: netcmc_1.0.zip, r-release: netcmc_1.0.zip, r-oldrel: netcmc_1.0.1.zip
macOS binaries: r-release (arm64): netcmc_1.0.tgz, r-oldrel (arm64): netcmc_1.0.tgz, r-release (x86_64): netcmc_1.0.tgz, r-oldrel (x86_64): netcmc_1.0.tgz
Old sources: netcmc archive

Linking:

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