R package spBFA: Spatial Bayesian Factor Analysis. Implements a spatial Bayesian non-parametric factor analysis model with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). Spatial correlation is introduced in the columns of the factor loadings matrix using a Bayesian non-parametric prior, the probit stick-breaking process. Areal spatial data is modeled using a conditional autoregressive (CAR) prior and point-referenced spatial data is treated using a Gaussian process. The response variable can be modeled as Gaussian, probit, Tobit, or Binomial (using Polya-Gamma augmentation). Temporal correlation is introduced for the latent factors through a hierarchical structure and can be specified as exponential or first-order autoregressive.
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References in zbMATH (referenced in 1 article , 1 standard article )
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- Samuel I. Berchuck, Mark Janko, Felipe A. Medeiros, William Pan, Sayan Mukherjee: Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces (2019) arXiv