geosimilarity

Understanding spatial association is essential for spatial statistical inference, including factor exploration and spatial prediction. Geographically optimal similarity (GOS) model is an effective method for spatial prediction, as described in Yongze Song (2022) doi:10.1007/s11004-022-10036-8. GOS was developed based on the geographical similarity principle, as described in Axing Zhu (2018) doi:10.1080/19475683.2018.1534890. GOS has advantages in more accurate spatial prediction using fewer samples and critically reduced prediction uncertainty.

Last 30 days

This package has been downloaded 271 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 1 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 3,103 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Sep 11, 2024 with 71 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.