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 the corresponding paper on arXiv
by Berchuck et al (2018): "Diagnosing Glaucoma Progression with Visual Field Data Using a
Spatiotemporal Boundary Detection Method",
|License||GPL (>= 3)|
|Package repository||View on GitHub|
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