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",
Fixed a bug related to the use of the truncated normal from the
Updated the vignette.
STBDwDM() functionality, related to the adjacency weights that can be used. A new option is included,
Weights allows for the
binary weights of the Lee and Mitchell (2011) specification.
PlotVFTimeSeries() functionality, related to the location specific regression line (including
Default bounds for lower and upper bounds for
Phi are now specific to the temporal correlation structure specification (i.e., if
"ar1" the bounds are now appropriate).
Adjusted the truncated normal random sampler to allow it to use the inverse CDF method or accept-reject method of the
msm package when necessary.
Tidying of the help pages.