Package: spBFA
Type: Package
Title: Spatial Bayesian Factor Analysis
Version: 1.3
Date: 2023-03-21
Authors@R: person("Samuel I.", "Berchuck", email = "sib2@duke.edu", role = c("aut", "cre"))
Description: 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), 
    <arXiv:1911.04337>. The paper is in press at the journal Bayesian Analysis.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
RoxygenNote: 7.2.1
NeedsCompilation: yes
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
Suggests: coda, classInt, knitr, rmarkdown, womblR (>= 1.0.3)
LinkingTo: Rcpp, RcppArmadillo (>= 0.7.500.0.0)
VignetteBuilder: knitr
Language: en-US
Packaged: 2023-03-21 17:58:18 UTC; sib2
Author: Samuel I. Berchuck [aut, cre]
Maintainer: Samuel I. Berchuck <sib2@duke.edu>
Repository: CRAN
Date/Publication: 2023-03-21 18:30:02 UTC
Built: R 4.2.0; aarch64-apple-darwin20; 2023-07-11 00:24:02 UTC; unix
Archs: spBFA.so.dSYM
