stgam

A framework for specifying spatially, temporally and spatially-and-temporally varying coefficient models using Generalized Additive Models with Gaussian Process smooths. The smooths are parameterised with location and / or time attributes. Importantly the framework supports the investigation of the presence and nature of any space-time dependencies in the data, allows the user to evaluate different model forms (specifications) and to pick the most probable model or to combine multiple varying coefficient models using Bayesian Model Averaging. For more details see: Brunsdon et al (2023) doi:10.4230/LIPIcs.GIScience.2023.17, Comber et al (2023) doi:10.4230/LIPIcs.GIScience.2023.22 and Comber et al (2024) doi:10.1080/13658816.2023.2270285.

Last 30 days

This package has been downloaded 169 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 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 2,030 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Nov 13, 2024 with 46 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.