doi:10.48550/arXiv.2407.12633>, extending the approach of Zhang, B., Sang, H., Luo, Z. T., and Huang, H. (2023) <doi:10.1214/22-AOAS1643>. The package includes tools for model fitting, convergence diagnostics, visualization, and summarization of clustering results.">

sfclust: Bayesian Spatial Functional Clustering (original) (raw)

Bayesian clustering of spatial regions with similar functional shapes using spanning trees and latent Gaussian models. The method enforces spatial contiguity within clusters and supports a wide range of latent Gaussian models, including non-Gaussian likelihoods, via the R-INLA framework. The algorithm is based on Zhong, R., Chacón-Montalván, E. A., and Moraga, P. (2024) <doi:10.48550/arXiv.2407.12633>, extending the approach of Zhang, B., Sang, H., Luo, Z. T., and Huang, H. (2023) <doi:10.1214/22-AOAS1643>. The package includes tools for model fitting, convergence diagnostics, visualization, and summarization of clustering results.

Version: 1.0.1
Depends: R (≥ 4.1.0)
Imports: cubelyr, igraph, sf, SparseM, stars, dplyr, methods, Matrix
Suggests: ggplot2, ggraph, class, fda, purrr, INLA, knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2025-05-19
DOI: 10.32614/CRAN.package.sfclust
Author: Erick A. Chacón-MontalvánORCID iD [aut, cre], Ruiman Zhong ORCID iD [aut], Paula Moraga ORCID iD [aut]
Maintainer: Erick A. Chacón-Montalván <erick.chaconmontalvan at kaust.edu.sa>
License: MIT + file
NeedsCompilation: no
Additional_repositories: https://inla.r-inla-download.org/R/stable
Materials: README
CRAN checks: sfclust results

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