geocausal

Spatio-temporal causal inference based on point process data. You provide the raw data of locations and timings of treatment and outcome events, specify counterfactual scenarios, and the package estimates causal effects over specified spatial and temporal windows. See Papadogeorgou, et al. (2022) doi:10.1111/rssb.12548 and Mukaigawara, et al. (2024) doi:10.31219/osf.io/5kc6f.

Last 30 days

This package has been downloaded 327 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 7 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 4,158 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 Mar 28, 2024 with 52 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.