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 |
| 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 |
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