Package: womblR
Type: Package
Title: Spatiotemporal Boundary Detection Model for Areal Unit Data
Version: 1.0.6
Description: Implements a spatiotemporal boundary detection model with a dissimilarity
    metric for areal data with inference in a Bayesian setting using Markov chain
    Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget),
    probit or Tobit link and spatial correlation is introduced at each time point
    through a conditional autoregressive (CAR) prior. Temporal correlation is introduced
    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 "Diagnosing Glaucoma 
    Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method", 
    by Berchuck et al (2019) <doi:10.1080/01621459.2018.1537911>.
Date: 2025-09-26
Authors@R: person("Samuel I.", "Berchuck", email = "sib2@duke.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-5705-3144"))
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
NeedsCompilation: yes
Depends: R (>= 3.0.2)
Imports: graphics, grDevices, msm (>= 1.0.0), mvtnorm (>= 1.0-0), Rcpp
        (>= 0.12.9), stats, utils
Suggests: coda, classInt, knitr, rmarkdown
LinkingTo: Rcpp, RcppArmadillo (>= 0.7.500.0.0)
VignetteBuilder: knitr
Packaged: 2025-09-26 20:40:39 UTC; sib2
Author: Samuel I. Berchuck [aut, cre] (ORCID:
    <https://orcid.org/0000-0001-5705-3144>)
Maintainer: Samuel I. Berchuck <sib2@duke.edu>
Repository: CRAN
Date/Publication: 2025-09-26 21:10:02 UTC
Built: R 4.4.3; x86_64-w64-mingw32; 2026-02-02 04:21:37 UTC; windows
Archs: x64
