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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
DOI: 10.32614/CRAN.package.SpatialRoMLE
Author: Vural Yildirim ORCID iD [aut, cre], Yeliz Mert Kantar ORCID iD [aut, ths]
Maintainer: Vural Yildirim <vurall_yildirim at hotmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: SpatialRoMLE results

Documentation:

Reference manual: SpatialRoMLE.pdf

Downloads:

Package source: SpatialRoMLE_0.1.0.tar.gz
Windows binaries: r-devel: SpatialRoMLE_0.1.0.zip, r-release: SpatialRoMLE_0.1.0.zip, r-oldrel: SpatialRoMLE_0.1.0.zip
macOS binaries: r-release (arm64): SpatialRoMLE_0.1.0.tgz, r-oldrel (arm64): SpatialRoMLE_0.1.0.tgz, r-release (x86_64): SpatialRoMLE_0.1.0.tgz, r-oldrel (x86_64): SpatialRoMLE_0.1.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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