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