{ "_id": "6a129313acfb0bcc41d0b7d3", "Package": "SCDA", "Type": "Package", "Title": "Spatially-Clustered Data Analysis", "Version": "0.0.2", "Description": "Contains functions for statistical data analysis based on\nspatially-clustered techniques. The package allows estimating\nthe spatially-clustered spatial regression models presented in\nCerqueti, Maranzano \\& Mattera (2024), \"Spatially-clustered\nspatial autoregressive models with application to agricultural\nmarket concentration in Europe\", arXiv preprint 2407.15874\n. Specifically, the current\nrelease allows the estimation of the spatially-clustered linear\nregression model (SCLM), the spatially-clustered spatial\nautoregressive model (SCSAR), the spatially-clustered spatial\nDurbin model (SCSEM), and the spatially-clustered linear\nregression model with spatially-lagged exogenous covariates\n(SCSLX). From release 0.0.2, the library contains functions to\nestimate spatial clustering based on Adiajacent Matrix K-Means\n(AMKM) as described in Zhou, Liu \\& Zhu (2019), \"Weighted\nadjacent matrix for K-means clustering\", Multimedia Tools and\nApplications, 78 (23) .", "License": "GPL (>= 2)", "Encoding": "UTF-8", "Language": "en-US", "RoxygenNote": "7.3.1", "LazyData": "true", "Maintainer": "Paolo Maranzano ", "Authors@R": "c(person(given = \"Paolo\",\nfamily = \"Maranzano\",\nrole = c(\"aut\",\"cre\",\"cph\"),\nemail = \"pmaranzano.ricercastatistica@gmail.com\",\ncomment = c(ORCID = \"0000-0002-9228-2759\")),\nperson(given = \"Raffaele\",\nfamily = \"Mattera\",\nrole = c(\"aut\",\"cph\"),\nemail = \"raffaele.mattera@uniroma1.it\",\ncomment = c(ORCID = \"0000-0001-8770-7049\")),\nperson(given = \"Camilla\",\nfamily = \"Lionetti\",\nrole = c(\"aut\",\"cph\"),\nemail = \"lionetticamilla511@gmail.com\"),\nperson(given = \"Francesco\",\nfamily = \"Caccia\",\nrole = 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