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: 0.3-0
Depends: R (≥ 3.5.0), stats, utils
Imports: Rcpp (≥ 1.0.8)
LinkingTo: Rcpp, RcppArmadillo
Published: 2023-05-26
DOI: 10.32614/CRAN.package.hgwrr
Author: Yigong Hu, Richard Harris, Richard Timmerman
Maintainer: Yigong Hu <yigong.hu at bristol.ac.uk>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/HPDell/hgwr/, https://hpdell.github.io/hgwr/
NeedsCompilation: yes
SystemRequirements: GNU make
CRAN checks: hgwrr results


Reference manual: hgwrr.pdf


Package source: hgwrr_0.3-0.tar.gz
Windows binaries: r-devel: hgwrr_0.3-0.zip, r-release: hgwrr_0.3-0.zip, r-oldrel: hgwrr_0.3-0.zip
macOS binaries: r-release (arm64): hgwrr_0.3-0.tgz, r-oldrel (arm64): hgwrr_0.3-0.tgz, r-release (x86_64): hgwrr_0.3-0.tgz, r-oldrel (x86_64): hgwrr_0.3-0.tgz
Old sources: hgwrr archive


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