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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "spCP" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'spCP: Spatially Varying Change Points'
version: '1.3'
doi: 10.32614/CRAN.package.spCP
abstract: '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", <arXiv:1811.11038>.'
authors:
- family-names: Berchuck
  given-names: Samuel I.
  email: sib2@duke.edu
repository: https://berchuck.r-universe.dev
commit: 2744ef43bb2f7bc50fba496670a2139bf54e5610
date-released: '2022-09-04'
contact:
- family-names: Berchuck
  given-names: Samuel I.
  email: sib2@duke.edu