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hgwrr: Hierarchical and Geographically Weighted Regression

This model divides coefficients into three types, i.e., local fixed effects, global fixed effects, and random effects (Hu et al., 2022)<doi:10.1177/23998083211063885>. If data have spatial hierarchical structures (especially are overlapping on some locations), it is worth trying this model to reach better fitness.

Version: 0.6-1
Depends: R (≥ 3.5.0), sf, stats, utils, MASS
Imports: Rcpp (≥ 1.0.8)
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0), furrr, progressr
Published: 2024-11-16
DOI: 10.32614/CRAN.package.hgwrr
Author: Yigong Hu [aut, cre], Richard Harris [aut], Richard Timmerman [aut]
Maintainer: Yigong Hu <yigong.hu at bristol.ac.uk>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/HPDell/hgwrr/, https://hpdell.github.io/hgwrr/
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: NEWS
CRAN checks: hgwrr results

Documentation:

Reference manual: hgwrr.pdf
Vignettes: hgwrr (source, R code)

Downloads:

Package source: hgwrr_0.6-1.tar.gz
Windows binaries: r-devel: hgwrr_0.6-1.zip, r-release: hgwrr_0.6-1.zip, r-oldrel: hgwrr_0.6-1.zip
macOS binaries: r-release (arm64): hgwrr_0.6-1.tgz, r-oldrel (arm64): hgwrr_0.6-1.tgz, r-release (x86_64): hgwrr_0.6-1.tgz, r-oldrel (x86_64): hgwrr_0.6-1.tgz
Old sources: hgwrr archive

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.