netcmc
1.0.2Spatio-Network Generalised Linear Mixed Models for Areal Unit and Network Data
Overview
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).
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3 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- archivedRemoved from CRAN2025-09-17email to the maintainer is undeliverable
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 1.0.22022-11-08 · diff ↗
- 1.0.12022-06-28 · diff ↗
- RR 4.2.0 released · 2022-04-22
- 1.02022-02-07
- RR 4.1.0 released · 2021-05-18
Package metadata
- Total releases
- 3
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 4.0.0
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