netcmc

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).

Last 30 days

This package has been downloaded 184 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 2 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 2,480 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 11, 2024 with 32 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.