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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1.0 by Samuel I. Berchuck, 3 months ago

Browse source code at

Authors: Samuel I. Berchuck [aut, cre]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports graphics, grDevices, msm, mvtnorm, pgdraw, Rcpp, stats, utils

Suggests coda, classInt, knitr, rmarkdown, womblR

Linking to Rcpp, RcppArmadillo

See at CRAN