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. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in "Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces", by Berchuck et al (2019), <arXiv:1911.04337>. The paper is in press at the journal Bayesian Analysis.

Version: 1.1
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: 2021-04-27
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

Downloads:

Reference manual: spBFA.pdf
Vignettes: spBFA-example
Package source: spBFA_1.1.tar.gz
Windows binaries: r-devel: spBFA_1.1.zip, r-release: spBFA_1.1.zip, r-oldrel: spBFA_1.1.zip
macOS binaries: r-release: spBFA_1.1.tgz, r-oldrel: spBFA_1.1.tgz
Old sources: spBFA archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=spBFA to link to this page.