spBFA-package

spBFA

<code>spBFA</code> is a package for Bayesian spatial factor analysis. A corresponding manuscript is forthcoming.

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:10.1214/20-BA1253> in Bayesian Analysis.

Samuel I. Berchuck

Spatial Bayesian Factor Analysis

spBFA function

Samuel I. Berchuck <a href="/link/sib2%40duke.edu?package=spBFA&version=1.5.0" data-mini-rdoc="spBFA::sib2@duke.edu">sib2@duke.edu</a>

Author

spBFA — spBFA

Samuel I. Berchuck <a href='mailto:sib2@duke.edu'>sib2@duke.edu</a>

spBFA: spBFA

Description

Arguments

Author