spCP: Spatially Varying Change Points

Implements a spatially varying change point model with unique intercepts, slopes, variance intercepts and slopes, and change points at each location. Inference is within the Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and the five spatially varying parameter are modeled jointly using a multivariate conditional autoregressive (MCAR) prior. The MCAR is a unique process that allows for a dissimilarity metric to dictate the local spatial dependencies. 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): "A spatially varying change points model for monitoring glaucoma progression using visual field data", <doi:10.48550/arXiv.1811.11038>.

Version:	1.3
Depends:	R (≥ 3.0.2)
Imports:	graphics, grDevices, msm (≥ 1.0.0), mvtnorm (≥ 1.0-0), Rcpp (≥ 0.12.9), stats, utils
LinkingTo:	Rcpp, RcppArmadillo (≥ 0.7.500.0.0)
Suggests:	coda, classInt, knitr, rmarkdown, womblR (≥ 1.0.3)
Published:	2022-09-05
Author:	Samuel I. Berchuck [aut, cre]
Maintainer:	Samuel I. Berchuck <sib2 at duke.edu>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
CRAN checks:	spCP results

Documentation:

Reference manual:	spCP.pdf
Vignettes:	spCP-example

Downloads:

Package source:	spCP_1.3.tar.gz
Windows binaries:	r-devel: spCP_1.3.zip, r-release: spCP_1.3.zip, r-oldrel: spCP_1.3.zip
macOS binaries:	r-release (arm64): spCP_1.3.tgz, r-oldrel (arm64): spCP_1.3.tgz, r-release (x86_64): spCP_1.3.tgz, r-oldrel (x86_64): spCP_1.3.tgz
Old sources:	spCP archive

Linking:

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