Contains functions for statistical data analysis based on spatially-clustered techniques. The package allows estimating the spatially-clustered spatial regression models presented in Cerqueti, Maranzano \& Mattera (2024), "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe", arXiv preprint 2407.15874 <doi:10.48550/arXiv.2407.15874>. Specifically, the current release allows the estimation of the spatially-clustered linear regression model (SCLM), the spatially-clustered spatial autoregressive model (SCSAR), the spatially-clustered spatial Durbin model (SCSEM), and the spatially-clustered linear regression model with spatially-lagged exogenous covariates (SCSLX). From release 0.0.2, the library contains functions to estimate spatial clustering based on Adiajacent Matrix K-Means (AMKM) as described in Zhou, Liu \& Zhu (2019), "Weighted adjacent matrix for K-means clustering", Multimedia Tools and Applications, 78 (23) <doi:10.1007/s11042-019-08009-x>.
Version: 0.0.2 Depends: R (≥ 3.5.0) Imports: spatialreg, sp, spdep, utils, rlang, performance, stats, methods, dplyr, sf, NbClust, ggplot2, ggspatial Suggests: tidyverse Published: 2024-10-22 DOI: 10.32614/CRAN.package.SCDA Author: Paolo Maranzano [aut, cre, cph], Raffaele Mattera [aut, cph], Camilla Lionetti [aut, cph], Francesco Caccia [aut, cph] Maintainer: Paolo Maranzano <pmaranzano.ricercastatistica at gmail.com> License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] NeedsCompilation: no Language: en-US Citation: SCDA citation info CRAN checks: SCDA results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=SCDA to link to this page.
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