Package: hgwrr 0.6-1
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.
Authors:
hgwrr_0.6-1.tar.gz
hgwrr_0.6-1.tar.gz(r-4.5-noble)hgwrr_0.6-1.tar.gz(r-4.4-noble)
hgwrr.pdf |hgwrr.html✨
hgwrr/json (API)
NEWS
# Install 'hgwrr' in R: |
install.packages('hgwrr', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/hpdell/hgwrr/issues
Pkgdown site:https://hpdell.github.io
- mulsam.test - Simulated Spatial Multisampling Data For Test
- multisampling - Large Scale Simulated Spatial Multisampling Data
- wuhan.hp - Wuhan Second-hand House Price and POI Data
Last updated 1 months agofrom:8b09e6d7f5. Checks:OK: 1 NOTE: 1. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 25 2024 |
R-4.5-linux-x86_64 | NOTE | Dec 25 2024 |
Exports:hgwrhgwr_fitmake_dummymake_dummy_extractspatial_hetero_testspatial_hetero_test_data
Dependencies:classclassIntDBIe1071KernSmoothmagrittrMASSproxyRcppRcppArmadillos2sfunitswk