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: | 0.3.4 |
| 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: | 2025-01-07 |
| 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 + fileLICENSE |
| URL: | https://github.com/mmukaigawara/geocausal |
| NeedsCompilation: | no |
| Materials: | README,NEWS |
| CRAN checks: | geocausal results |