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Nonparametric data-driven approach to discovering heterogeneous subgroups in a selection-on-observables framework. aggTrees allows researchers to assess whether there exists relevant heterogeneity in treatment effects by generating a sequence of optimal groupings, one for each level of granularity. For each grouping, we obtain point estimation and inference about the Group Average Treatment Effects. Please reference the use as Di Francesco (2022) <doi:10.2139/ssrn.4304256>.
| Version: | 2.0.2 |
| Depends: | R (≥ 2.10) |
| Imports: | boot, broom, car, caret, estimatr, grf, rpart, rpart.plot, stats, stringr |
| Suggests: | knitr, rmarkdown |
| Published: | 2023-09-20 |
| Author: | Riccardo Di Francesco [aut, cre, cph] |
| Maintainer: | Riccardo Di Francesco <difrancesco.riccardo96 at gmail.com> |
| License: | MIT + file LICENSE |
| URL: | https://riccardo-df.github.io/aggTrees/ |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | aggTrees results |
| Reference manual: | aggTrees.pdf |
| Vignettes: |
Short Tutorial |
| Package source: | aggTrees_2.0.2.tar.gz |
| Windows binaries: | r-devel: aggTrees_2.0.2.zip, r-release: aggTrees_2.0.2.zip, r-oldrel: aggTrees_2.0.2.zip |
| macOS binaries: | r-release (arm64): aggTrees_2.0.2.tgz, r-oldrel (arm64): aggTrees_2.0.2.tgz, r-release (x86_64): aggTrees_2.0.2.tgz, r-oldrel (x86_64): aggTrees_2.0.2.tgz |
| Old sources: | aggTrees archive |
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