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:	0.2-3
Depends:	R (≥ 3.5.0), stats, utils
Imports:	Rcpp (≥ 1.0.8)
LinkingTo:	Rcpp, RcppArmadillo
Published:	2022-06-15
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)]
NeedsCompilation:	yes
SystemRequirements:	GNU make
CRAN checks:	hgwrr results

Documentation:

Reference manual:

hgwrr.pdf

Downloads:

Package source:	hgwrr_0.2-3.tar.gz
Windows binaries:	r-devel: hgwrr_0.2-3.zip, r-release: hgwrr_0.2-3.zip, r-oldrel: hgwrr_0.2-3.zip
macOS binaries:	r-release (arm64): hgwrr_0.2-3.tgz, r-oldrel (arm64): hgwrr_0.2-3.tgz, r-release (x86_64): hgwrr_0.2-3.tgz, r-oldrel (x86_64): hgwrr_0.2-3.tgz
Old sources:	hgwrr archive

Linking:

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