{ "_id": "6a1eeab2b401979e734120ad", "Package": "spCP", "Type": "Package", "Title": "Spatially Varying Change Points", "Version": "1.4.0", "Date": "2025-09-29", "Authors@R": "person(\"Samuel I.\", \"Berchuck\", email = \"sib2@duke.edu\", role = c(\"aut\", \"cre\"))", "Description": "Implements a spatially varying change point model with\nunique intercepts, slopes, variance intercepts and slopes, and\nchange points at each location. Inference is within the\nBayesian setting using Markov chain Monte Carlo (MCMC). The\nresponse variable can be modeled as Gaussian (no nugget),\nprobit or Tobit link and the five spatially varying parameter\nare modeled jointly using a multivariate conditional\nautoregressive (MCAR) prior. The MCAR is a unique process that\nallows for a dissimilarity metric to dictate the local spatial\ndependencies. Full details of the package can be found in the\naccompanying vignette. Furthermore, the details of the package\ncan be found in the corresponding paper published in Spatial\nStatistics by Berchuck et al (2019): \"A spatially varying\nchange points model for monitoring glaucoma progression using\nvisual field data\", .", "License": "GPL (>= 2)", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.2", "NeedsCompilation": "yes", "VignetteBuilder": "knitr", "Repository": "https://berchuck.r-universe.dev", "Date/Publication": "2025-09-30 12:35:50 UTC", "RemoteUrl": "https://github.com/berchuck/spcp", "RemoteRef": "HEAD", "RemoteSha": "75b325643afaba406d1ead0c9425aa1ebb3514fa", "Packaged": { "Date": "2026-05-19 09:59:29 UTC", "User": "root" }, "Author": "Samuel I. Berchuck [aut, cre]", "Maintainer": "Samuel I. 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