Package: mgwnbr 0.3.0

mgwnbr: Multiscale Geographically Weighted Negative Binomial Regression

Fits a geographically weighted regression model with different scales for each covariate. Uses the negative binomial distribution as default, but also accepts the normal, Poisson, or logistic distributions. Can fit the global versions of each regression and also the geographically weighted alternatives with only one scale, since they are all particular cases of the multiscale approach. Hanchen Yu (2024). "Exploring Multiscale Geographically Weighted Negative Binomial Regression", Annals of the American Association of Geographers <doi:10.1080/24694452.2023.2289986>. Fotheringham AS, Yang W, Kang W (2017). "Multiscale Geographically Weighted Regression (MGWR)", Annals of the American Association of Geographers <doi:10.1080/24694452.2017.1352480>. Da Silva AR, Rodrigues TCV (2014). "Geographically Weighted Negative Binomial Regression - incorporating overdispersion", Statistics and Computing <doi:10.1007/s11222-013-9401-9>.

Authors:Juliana Rosa [aut, cre], Jéssica Vasconcelos [aut], Alan da Silva [aut]

mgwnbr_0.3.0.tar.gz
mgwnbr_0.3.0.zip(r-4.7)mgwnbr_0.3.0.zip(r-4.6)mgwnbr_0.3.0.zip(r-4.5)
mgwnbr_0.3.0.tgz(r-4.6-any)mgwnbr_0.3.0.tgz(r-4.5-any)
mgwnbr_0.3.0.tar.gz(r-4.7-any)mgwnbr_0.3.0.tar.gz(r-4.6-any)
mgwnbr_0.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
mgwnbr/json (API)

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

Bug tracker:https://github.com/julianamrosa/mgwnbr/issues

Datasets:

On CRAN:

Conda:

2.48 score 3 scripts 215 downloads 1 exports 2 dependencies

Last updated from:36e9cc7365. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK123
source / vignettesOK162
linux-release-x86_64OK109
macos-release-arm64OK128
macos-oldrel-arm64OK143
windows-develOK88
windows-releaseOK70
windows-oldrelOK75
wasm-releaseOK90

Exports:mgwnbr

Dependencies:latticesp