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: | 0.6-2 |
| 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: | 2025-09-28 |
| 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 |
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