Package: SpatialRoMLE 0.1.1.1

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>).

Authors:Vural Yildirim [aut, cre], Yeliz Mert Kantar [aut, ths]

SpatialRoMLE_0.1.1.1.tar.gz
SpatialRoMLE_0.1.1.1.zip(r-4.7)SpatialRoMLE_0.1.1.1.zip(r-4.6)SpatialRoMLE_0.1.1.1.zip(r-4.5)
SpatialRoMLE_0.1.1.1.tgz(r-4.6-any)SpatialRoMLE_0.1.1.1.tgz(r-4.5-any)
SpatialRoMLE_0.1.1.1.tar.gz(r-4.7-any)SpatialRoMLE_0.1.1.1.tar.gz(r-4.6-any)
SpatialRoMLE_0.1.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION
card.svg |card.png
SpatialRoMLE/json (API)

# Install 'SpatialRoMLE' in R:
install.packages('SpatialRoMLE', repos = c('https://wyilldirim.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/wyilldirim/spatialromle/issues

Datasets:

On CRAN:

Conda:

2.70 score 219 downloads 1 exports 0 dependencies

Last updated from:c84b4bf610. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK99
source / vignettesOK128
linux-release-x86_64OK92
macos-release-arm64OK142
macos-oldrel-arm64OK216
windows-develOK62
windows-releaseOK61
windows-oldrelOK54
wasm-releaseOK101

Exports:RoMLE.error

Dependencies: