SpatialRoMLE: Robust Maximum Likelihood Estimation for Spatial Error Model

Provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, <doi:10.1080/00949655.2020.1740223>).

Version: 0.1.0
Depends: R (≥ 2.10)
Published: 2020-03-31
Author: Vural Yildirim ORCID iD [aut, cre], Yeliz Mert Kantar ORCID iD [aut, ths]
Maintainer: Vural Yildirim <vurall_yildirim at>
License: GPL-3
NeedsCompilation: no
CRAN checks: SpatialRoMLE results


Reference manual: SpatialRoMLE.pdf
Package source: SpatialRoMLE_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: SpatialRoMLE_0.1.0.tgz, r-oldrel: SpatialRoMLE_0.1.0.tgz


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