hgwrr-package: HGWR: Hierarchical and Geographically Weighted Regression

In hgwrr: Hierarchical and Geographically Weighted Regression

HGWR: Hierarchical and Geographically Weighted Regression

Description

An R and C++ implementation of Hierarchical and Geographically Weighted Regression (HGWR) model is provided in this package. This model divides coefficients into three types: local fixed effects, global fixed effects, and random effects. If data have spatial hierarchical structures (especially are overlapping on some locations), it is worth trying this model to reach better fitness.

Details

This package was not yet installed at build time.

Note

Acknowledgement: We gratefully acknowledge support from China Scholarship Council.

Author(s)

Yigong Hu, Richard Harris, Richard Timmerman

References

Hu, Y., Lu, B., Ge, Y., Dong, G., 2022. Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression. Environment and Planning B: Urban Analytics and City Science. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/23998083211063885")}

hgwrr documentation built on April 4, 2025, 3:57 a.m.