aggTrees: Aggregation Trees

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:	2.1.0
Depends:	R (≥ 2.10)
Imports:	boot, broom, car, caret, estimatr, grf, rpart, rpart.plot, stats, stringr
Suggests:	knitr, rmarkdown
Published:	2024-09-09
DOI:	10.32614/CRAN.package.aggTrees
Author:	Riccardo Di Francesco [aut, cre, cph]
Maintainer:	Riccardo Di Francesco <difrancesco.riccardo96 at gmail.com>
BugReports:	https://github.com/riccardo-df/aggTrees/issues
License:	MIT + file LICENSE
URL:	https://riccardo-df.github.io/aggTrees/
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	aggTrees results

Documentation:

Reference manual:	aggTrees.pdf
Vignettes:	Short Tutorial (source, R code) Inference (source, R code)

Downloads:

Package source:	aggTrees_2.1.0.tar.gz
Windows binaries:	r-devel: aggTrees_2.1.0.zip, r-release: aggTrees_2.1.0.zip, r-oldrel: aggTrees_2.1.0.zip
macOS binaries:	r-release (arm64): aggTrees_2.1.0.tgz, r-oldrel (arm64): aggTrees_2.1.0.tgz, r-release (x86_64): aggTrees_2.1.0.tgz, r-oldrel (x86_64): aggTrees_2.1.0.tgz
Old sources:	aggTrees archive

Linking:

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