Estimating spatial probit models in R

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.