Bayesian Spatial Item Factor Analysis

Fits spatial item factor analysis models for binary responses, following Chacón-Montalván, E. A., Parry, L., Giorgi, E., Torres, P., Orellana, J. D. Y., Moraga, P., and Taylor, B. M. (2025) , using full Bayesian inference via adaptive Metropolis-Hastings within Gibbs sampling and auxiliary variables with a probit link function. The latent factors are modelled as the sum of a predictor effect, a multivariate Gaussian process capturing spatial dependence, and a multivariate non-spatial term, allowing users to fit and predict spatially-referenced constructs such as food insecurity or socioeconomic indices. Standard exploratory and confirmatory item factor analysis are supported as particular cases, simply by dropping the spatial structure.