SCDA - Spatially-Clustered Data Analysis
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>.