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:
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