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 |
| 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:
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