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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