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>.
| Version: |
0.3.3 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
data.table, dplyr, furrr, ggplot2, ggpubr, latex2exp, mclust, progressr, purrr, sf, spatstat.explore, spatstat.geom, spatstat.model, spatstat.univar, terra, tidyr, tidyselect, tidyterra |
| Suggests: |
elevatr, geosphere, gridExtra, ggthemes, knitr, readr, gridGraphics |
| Published: |
2024-12-03 |
| DOI: |
10.32614/CRAN.package.geocausal |
| Author: |
Mitsuru Mukaigawara
 [cre, aut],
Lingxiao Zhou [aut],
Georgia Papadogeorgou
[aut],
Jason Lyall [aut],
Kosuke Imai [aut] |
| Maintainer: |
Mitsuru Mukaigawara <mitsuru_mukaigawara at g.harvard.edu> |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/mmukaigawara/geocausal |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
geocausal results |