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: | 0.1.1.1 |
| Depends: | R (≥ 2.10) |
| Published: | 2025-07-20 |
| DOI: | 10.32614/CRAN.package.SpatialRoMLE |
| Author: | Vural Yildirim 
[aut, cre],
Yeliz Mert Kantar
[aut, ths] |
| Maintainer: | Vural Yildirim <vurall_yildirim at hotmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | SpatialRoMLE results |
Documentation:
| Reference manual: | SpatialRoMLE.html , SpatialRoMLE.pdf |
Downloads:
| Package source: | SpatialRoMLE_0.1.1.1.tar.gz |
| Windows binaries: | r-devel: SpatialRoMLE_0.1.1.1.zip, r-release: SpatialRoMLE_0.1.1.1.zip, r-oldrel: SpatialRoMLE_0.1.1.1.zip |
| macOS binaries: | r-release (arm64): SpatialRoMLE_0.1.1.1.tgz, r-oldrel (arm64): SpatialRoMLE_0.1.1.1.tgz, r-release (x86_64): SpatialRoMLE_0.1.1.1.tgz, r-oldrel (x86_64): SpatialRoMLE_0.1.1.1.tgz |
| Old sources: | SpatialRoMLE archive |
Linking:
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