Causal Inference with Spatio-Temporal Data R package geocausal version 0.4.2 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>. Software Depends: R (>= 3.5.0) Imports: crsuggest, ggthemes, data.table, dplyr, furrr, ggplot2, ggpubr, mclust, progressr, purrr, Rglpk, sf, spatstat.explore, spatstat.geom, spatstat.model, spatstat.univar, spatstat.random, terra, tibble, tidyr, tidyselect, tidyterra Suggests: elevatr, gridExtra, knitr, readr, gridGraphics Mitsuru Mukaigawara <mitsuru_mukaigawara@g.harvard.edu> Comprehensive R Archive Network (CRAN) Mitsuru Mukaigawara [cre, aut] (ORCID: <https://orcid.org/0000-0001-6530-2083>), Lingxiao Zhou [aut], Georgia Papadogeorgou [aut] (ORCID: <https://orcid.org/0000-0002-1982-2245>), Jason Lyall [aut] (ORCID: <https://orcid.org/0000-0001-9117-7503>), Kosuke Imai [aut] (ORCID: <https://orcid.org/0000-0002-2748-1022>) MIT + file LICENSE (https://CRAN.R-project.org/package=geocausal/LICENSE) 2026-06-26 application/tgz https://CRAN.R-project.org/package=geocausal doi:10.32614/CRAN.package.geocausal