womblR

Spatiotemporal Boundary Detection Model for Areal Unit Data

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.

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Description file content

Package
womblR
Type
Package
Title
Spatiotemporal Boundary Detection Model for Areal Unit Data
Version
1.0.2
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.
Date
2017-09-12
License
GPL (>= 3)
Encoding
UTF-8
LazyData
true
RoxygenNote
5.0.1
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
2017-09-12 18:51:19 UTC; Sam
Author
Samuel I. Berchuck [aut, cre]
Maintainer
Samuel I. Berchuck
Repository
CRAN
Date/Publication
2017-09-12 19:09:53 UTC

install.packages('womblR')

1.0.2

a month ago

Samuel I. Berchuck

GPL (>= 3)

Depends on

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

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