Hierarchical and Geographically Weighted Regression R package hgwrr version 0.6-2 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. Software 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, Yigong Hu <yigong.hu@bristol.ac.uk> Comprehensive R Archive Network (CRAN) Yigong Hu [aut, cre], Richard Harris [aut], Richard Timmerman [aut] GPL (>= 2) 2025-09-28 application/tgz https://CRAN.R-project.org/package=hgwrr doi:10.32614/CRAN.package.hgwrr