The R Journal: article published in 2013, volume 5:1

Estimating Spatial Probit Models in R
Stefan Wilhelm and Miguel Godinho de Matos , The R Journal (2013) 5:1, pages 130-143.

Abstract In this article we present the Bayesian estimation of spatial probit models in R and provide an implementation in the package spatialprobit. We show that large probit models can be estimated with sparse matrix representations and Gibbs sampling of a truncated multivariate normal distribution with the precision matrix. We present three examples and point to ways to achieve further performance gains through parallelization of the Markov Chain Monte Carlo approach.

Received: 2012-05-30; online 2013-06-03
CRAN packages: spBayes, spatial, geoR, sgeostat, spdep, sphet, sna, network, Matrix, sparseM, spatialprobit, McSpatial, LearnBayes, tmvtnorm, mvtnorm, igraph
CRAN Task Views implied by cited CRAN packages: Spatial, Bayesian, Distributions, Econometrics, SocialSciences, gR, Multivariate, Optimization, SpatioTemporal, Finance, Graphics, NumericalMathematics, Survival

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2013-013,
  author = {Stefan Wilhelm and Miguel Godinho de Matos},
  title = {{Estimating Spatial Probit Models in R}},
  year = {2013},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2013-013},
  url = {https://doi.org/10.32614/RJ-2013-013},
  pages = {130--143},
  volume = {5},
  number = {1}
}