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.

Version: 1.0
Depends: R (≥ 3.0.2)
Imports: graphics, grDevices, msm (≥ 1.0.0), mvtnorm (≥ 1.0-0), pgdraw (≥ 1.0), Rcpp (≥ 0.12.9), stats, utils
LinkingTo: Rcpp, RcppArmadillo (≥ 0.7.500.0.0)
Suggests: coda, classInt, knitr, rmarkdown, womblR (≥ 1.0.3)
Published: 2019-10-30
Author: Samuel I. Berchuck [aut, cre]
Maintainer: Samuel I. Berchuck <sib2 at duke.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Language: en-US
CRAN checks: spBFA results


Reference manual: spBFA.pdf
Vignettes: spBFA-example
Package source: spBFA_1.0.tar.gz
Windows binaries: r-devel: spBFA_1.0.zip, r-release: spBFA_1.0.zip, r-oldrel: spBFA_1.0.zip
macOS binaries: r-release: spBFA_1.0.tgz, r-oldrel: spBFA_1.0.tgz


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