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), <doi:10.48550/arXiv.1911.04337>. The paper is in press at the journal Bayesian Analysis.

Version:	1.3
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:	2023-03-21
DOI:	10.32614/CRAN.package.spBFA
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

Documentation:

Reference manual:	spBFA.pdf
Vignettes:	spBFA-example

Downloads:

Package source:	spBFA_1.3.tar.gz
Windows binaries:	r-devel: spBFA_1.3.zip, r-release: spBFA_1.3.zip, r-oldrel: spBFA_1.3.zip
macOS binaries:	r-release (arm64): spBFA_1.3.tgz, r-oldrel (arm64): spBFA_1.3.tgz, r-release (x86_64): spBFA_1.3.tgz, r-oldrel (x86_64): spBFA_1.3.tgz
Old sources:	spBFA archive

Linking:

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