Package: netcmc
Type: Package
Title: Spatio-Network Generalised Linear Mixed Models for Areal Unit
        and Network Data
Version: 1.0.2
Date: 2022-11-07
Author: George Gerogiannis, Mark Tranmer, Duncan Lee
Maintainer: George Gerogiannis <g.gerogiannis.1@research.gla.ac.uk>
Description: 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>).
License: GPL (>= 2)
Depends: R (>= 4.0.0), MCMCpack
Imports: Rcpp (>= 1.0.4), coda, ggplot2, mvtnorm, MASS
LinkingTo: Rcpp, RcppArmadillo, RcppProgress
LazyLoad: yes
Suggests: testthat, igraph, magic
NeedsCompilation: yes
Packaged: 2022-11-08 20:35:53 UTC; georg
Repository: CRAN
Date/Publication: 2022-11-08 22:30:15 UTC
Built: R 4.2.3; x86_64-w64-mingw32; 2024-04-24 02:05:09 UTC; windows
ExperimentalWindowsRuntime: ucrt
Archs: x64
