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spatemR
View on CRAN: Click
here
Download and install spatemR package within the R console
Install from CRAN:
install.packages("spatemR")
Install from Github:
library("remotes")
install_github("cran/spatemR") Install by package version:
library("remotes")
install_version("spatemR", "1.4.0") Attach the package and use:
library("spatemR")
Maintained by
Nelson Alirio Cruz Gutierrez
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[Scholar Profile | Author Map]
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First Published: 2025-04-03
Latest Update: 2025-06-03
Description:
Modeling spatial dependencies in dependent variables, extending traditional spatial regression approaches. It allows for the joint modeling of both the mean and the variance of the dependent variable, incorporating semiparametric effects in both models. Based on generalized additive models (GAM), the package enables the inclusion of non-parametric terms while maintaining the classical theoretical framework of spatial regression. Additionally, it implements the Generalized Spatial Autoregression (GSAR) model, which extends classical methods like logistic Spatial Autoregresive Models (SAR), probit Spatial Autoregresive Models (SAR), and Poisson Spatial Autoregresive Models (SAR), offering greater flexibility in modeling spatial dependencies and significantly improving computational efficiency and the statistical properties of the estimators. Related work includes: a) J.D. Toloza-Delgado, Melo O.O., Cruz N.A. (2024). "Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices". <doi:10.1016/j.spasta.2024.100864>. b) Cruz, N. A., Toloza-Delgado, J. D., Melo, O. O. (2024). "Generalized spatial autoregressive model". <doi:10.48550/arXiv.2412.00945>.
How to cite:
Nelson Alirio Cruz Gutierrez (2025). spatemR: Generalized Spatial Autoregresive Models for Mean and Variance. R package version 1.4.0, https://cran.r-project.org/web/packages/spatemR. Accessed 24 Oct. 2026.
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