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hgwrr  

Hierarchical and Geographically Weighted Regression
View on CRAN: Click here


Download and install hgwrr package within the R console
Install from CRAN:
install.packages("hgwrr")

Install from Github:
library("remotes")
install_github("cran/hgwrr")

Install by package version:
library("remotes")
install_version("hgwrr", "0.6-2")



Attach the package and use:
library("hgwrr")
Maintained by
Yigong Hu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-05-18
Latest Update: 2024-11-16
Description:
This model divides coefficients into three types, i.e., local fixed effects, global fixed effects, and random effects (Hu et al., 2022). If data have spatial hierarchical structures (especially are overlapping on some locations), it is worth trying this model to reach better fitness.
How to cite:
Yigong Hu (2022). hgwrr: Hierarchical and Geographically Weighted Regression. R package version 0.6-2, https://cran.r-project.org/web/packages/hgwrr. Accessed 09 Oct. 2026.
Previous versions and publish date:
(2026-07-09 07:47), 0.2-1 (2022-05-18 21:10), 0.2-2 (2022-05-20 09:10), 0.2-3 (2022-06-15 15:40), 0.3-0 (2023-05-26 12:10), 0.4-0 (2024-07-08 17:40), 0.5-0 (2024-07-28 17:00), 0.6-0 (2024-11-07 10:50), 0.6-1 (2024-11-16 12:50)
Other packages that cited hgwrr R package
View hgwrr citation profile
Other R packages that hgwrr depends, imports, suggests or enhances
Complete documentation for hgwrr
Functions, R codes and Examples using the hgwrr R package
Some associated functions: coef.hgwrm . fitted.hgwrm . hgwr . hgwrr-package . matrix2char . multisampling.large . multisampling . parse.formula . print.hgwrm . print.summary.hgwrm . print.table.md . residuals.hgwrm . summary.hgwrm . wuhan.hp . 
Some associated R codes: RcppExports.R . hgwr.R . parse.formula.R .  Full hgwrr package functions and examples
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