doi:10.1111/rssb.12548> and Mukaigawara, et al. (2024) <doi:10.31219/osf.io/5kc6f>.">

geocausal: Causal Inference with Spatio-Temporal Data (original) (raw)

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 MukaigawaraORCID iD [cre, aut], Lingxiao Zhou [aut], Georgia PapadogeorgouORCID iD [aut], Jason Lyall ORCID iD [aut], Kosuke Imai ORCID iD [aut]
Maintainer: Mitsuru Mukaigawara <mitsuru_mukaigawara at g.harvard.edu>
License: MIT + file
URL: https://github.com/mmukaigawara/geocausal
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: geocausal results

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