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.
- Version1.3
- R version≥ 3.0.2
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- Berchuck et al (2018): "A spatially varying change points model for monitoring glaucoma progression using visual field data"
- Last release09/05/2022
Documentation
Team
Samuel I. Berchuck
Insights
Last 30 days
This package has been downloaded 156 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 8 times.
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Last 365 days
This package has been downloaded 2,361 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Sep 11, 2024 with 29 downloads.
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Dependencies
- Imports3 packages
- Suggests5 packages
- Linking To2 packages