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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
 [cre, aut],
Georgia Papadogeorgou
[aut],
Jason Lyall [aut],
Kosuke Imai [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:
Downloads:
Linking:
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https://CRAN.R-project.org/package=geocausal
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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