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netcmc
View on CRAN: Click
here
Download and install netcmc package within the R console
Install from CRAN:
install.packages("netcmc")
Install from Github:
library("remotes")
install_github("cran/netcmc") Install by package version:
library("remotes")
install_version("netcmc", "1.0.2") Attach the package and use:
library("netcmc")
Maintained by
George Gerogiannis
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-02-07
Latest Update: 2022-11-08
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) ). Network structures are modelled by a set of random effects that reflect a multiple membership structure (Browne et al. (2001) ).
How to cite:
George Gerogiannis (2022). netcmc: Spatio-Network Generalised Linear Mixed Models for Areal Unit and Network Data. R package version 1.0.2, https://cran.r-project.org/web/packages/netcmc. Accessed 07 Oct. 2026.
Previous versions and publish date:
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Other R packages that netcmc depends,
imports, suggests or enhances
Complete documentation for netcmc
Functions, R codes and Examples using
the netcmc R package
Some associated functions: getAdjacencyMatrix . getMembershipMatrix . getTotalAltersByStatus . multiNet . multiNetLeroux . multiNetRand . netcmc-package . plot.netcmc . print.netcmc . summary.netcmc . uni . uniNet . uniNetLeroux . uniNetRand .
Some associated R codes: RcppExports.R . checkModelMCMCInputParameters.R . getAdjacencyMatrix.R . getBetaParameterConversion.R . getFormulaInfo.R . getInitialParameters.R . getMembershipMatrix.R . getStandardizedCovariates.R . getTotalAltersByStatus.R . multiNet.R . multiNetLeroux.R . multiNetRand.R . multivariateBinomialNetworkLeroux.R . multivariateBinomialNetworkRand.R . multivariateGaussianNetworkLerouxMH.R . multivariateGaussianNetworkRand.R . multivariatePoissonNetworkLeroux.R . multivariatePoissonNetworkRand.R . plot.netcmc.R . print.netcmc.R . summary.netcmc.R . uni.R . uniNet.R . uniNetLeroux.R . uniNetRand.R . univariateBinomialNetworkLeroux.R . univariateGaussianNetworkLerouxMH.R . univariatePoissonNetworkLeroux.R . Full netcmc package functions and examples
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