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).
| Version: |
0.0.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
spatialreg, sp, spdep, utils, rlang, performance, stats, methods, dplyr |
| Suggests: |
tidyverse, ggplot2, sf |
| Published: |
2024-10-14 |
| DOI: |
10.32614/CRAN.package.SCDA |
| Author: |
Paolo Maranzano 
[aut, cre, cph],
Raffaele Mattera
[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:
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