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