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: 0.3.0
Depends: R (≥ 3.5.0)
Imports: data.table, dplyr, furrr, ggplot2, ggpubr, latex2exp, mclust, progressr, purrr, sf, spatstat.explore, spatstat.geom, spatstat.model, terra, tidyr, tidyselect, tidyterra
Suggests: elevatr, geosphere, gridExtra, ggthemes, knitr, readr
Published: 2024-03-25
Author: Mitsuru Mukaigawara ORCID iD [cre, aut], Georgia Papadogeorgou ORCID iD [aut], Jason Lyall ORCID iD [aut], Kosuke Imai ORCID iD [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

Documentation:

Reference manual: geocausal.pdf

Downloads:

Package source: geocausal_0.3.0.tar.gz
Windows binaries: r-devel: geocausal_0.3.0.zip, r-release: geocausal_0.3.0.zip, r-oldrel: geocausal_0.3.0.zip
macOS binaries: r-release (arm64): geocausal_0.3.0.tgz, r-oldrel (arm64): geocausal_0.3.0.tgz, r-release (x86_64): geocausal_0.3.0.tgz
Old sources: geocausal archive

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