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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 |
| Reference manual: | spBFA.pdf |
| Vignettes: |
spBFA-example |
| 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 |
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