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.y i can reflect any binary outcome such as survival, a buy/don't buy decision or a class variable in binary classification problems.For identification, σ 2 is often set to σ 2 = 1.