spBFA (1.0)

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Spatial Bayesian Factor Analysis.

http://cran.r-project.org/web/packages/spBFA

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.

Maintainer: Samuel I. Berchuck
Author(s): Samuel I. Berchuck [aut, cre]

License: GPL (>= 2)

Uses: msm, mvtnorm, pgdraw, Rcpp, classInt, coda, knitr, rmarkdown, womblR

Released about 1 month ago.


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