geocausal: Causal Inference with Spatio-Temporal Data

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>.

Version: 0.3.1
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
Published: 2024-07-07
DOI: 10.32614/CRAN.package.geocausal
Author: Mitsuru Mukaigawara ORCID iD [cre, aut], Lingxiao Zhou [aut], Georgia Papadogeorgou ORCID iD [aut], Jason Lyall ORCID iD [aut], Kosuke Imai ORCID iD [aut]
Maintainer: Mitsuru Mukaigawara <mitsuru_mukaigawara at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: geocausal results


Reference manual: geocausal.pdf


Package source: geocausal_0.3.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): geocausal_0.3.1.tgz, r-oldrel (arm64): geocausal_0.3.1.tgz, r-release (x86_64): geocausal_0.3.1.tgz, r-oldrel (x86_64): geocausal_0.3.1.tgz
Old sources: geocausal archive


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