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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 |
| Reference manual: | netcmc.pdf |
| 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 |
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