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spCP  

Spatially Varying Change Points
View on CRAN: Click here


Download and install spCP package within the R console
Install from CRAN:
install.packages("spCP")

Install from Github:
library("remotes")
install_github("cran/spCP")

Install by package version:
library("remotes")
install_version("spCP", "1.4.0")



Attach the package and use:
library("spCP")
Maintained by
Samuel I. Berchuck
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-07-13
Latest Update: 2022-09-05
Description:
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>.
How to cite:
Samuel I. Berchuck (2018). spCP: Spatially Varying Change Points. R package version 1.4.0, https://cran.r-project.org/web/packages/spCP. Accessed 16 Jul. 2026.
Previous versions and publish date:
(2026-07-09 07:06), 1.0 (2018-07-13 18:20), 1.1 (2018-11-28 19:00), 1.2 (2018-12-05 08:40), 1.3 (2022-09-05 08:20)
Other packages that cited spCP R package
View spCP citation profile
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Complete documentation for spCP
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