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-any)mgwnbr_0.3.0.zip(r-4.6-any)mgwnbr_0.3.0.zip(r-4.5-any)
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:

2.00 score 3 scripts 256 downloads 1 exports 2 dependencies

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

TargetResultTimeFilesSyslog
linux-develOK99
source / vignettesOK126
linux-releaseOK106
macos-releaseOK150
macos-oldrelOK172
windows-develOK89
windows-releaseOK62
windows-oldrelOK53
wasm-releaseOK75

Exports:mgwnbr

Dependencies:latticesp