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)[https://doi.org/10.1177%2F23998083211063885]. If data have spatial hierarchical structures (especially are overlapping on some locations), it is worth trying this model to reach better fitness.
- Version0.6-1
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- Last release11/16/2024
Documentation
Team
Yigong Hu
Richard Harris
Show author detailsRolesAuthorRichard Timmerman
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 602 times in the last 30 days. More downloads than an obscure whitepaper, but not enough to bring down any servers. A solid effort! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 4 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 4,679 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Feb 05, 2025 with 62 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Depends2 packages
- Imports1 package
- Suggests5 packages
- Linking To2 packages