Package: aggTrees 2.1.0

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 (2024) <doi:10.48550/arXiv.2410.11408>.

Authors:Riccardo Di Francesco [aut, cre, cph]

aggTrees_2.1.0.tar.gz
aggTrees_2.1.0.zip(r-4.7)aggTrees_2.1.0.zip(r-4.6)aggTrees_2.1.0.zip(r-4.5)
aggTrees_2.1.0.tgz(r-4.6-any)aggTrees_2.1.0.tgz(r-4.5-any)
aggTrees_2.1.0.tar.gz(r-4.7-any)aggTrees_2.1.0.tar.gz(r-4.6-any)
aggTrees_2.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
aggTrees/json (API)

# Install 'aggTrees' in R:
install.packages('aggTrees', repos = c('https://riccardo-df.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/riccardo-df/aggtrees/issues

Pkgdown/docs site:https://riccardo-df.github.io

On CRAN:

Conda:

4.00 score 1 stars 5 scripts 322 downloads 13 exports 109 dependencies

Last updated from:6dd1d293eb. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK187
source / vignettesOK244
linux-release-x86_64OK164
macos-release-arm64OK102
macos-oldrel-arm64OK154
windows-develOK115
windows-releaseOK116
windows-oldrelOK130
wasm-releaseOK2905

Exports:avg_characteristics_rpartbalance_measuresbuild_aggtreecausal_ols_rpartdr_scoresestimate_rpartexpand_dfget_leavesinference_aggtreeleaf_membershipnode_membershipsample_splitsubtree

Dependencies:abindbackportsbootbroomcarcarDatacaretclasscliclockcodetoolscolorspacecowplotcpp11data.tableDerivdiagramDiceKrigingdigestdoBydplyre1071estimatrfarverforeachforecastFormulafracdifffuturefuture.applygenericsggplot2globalsgluegowergrfgtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlme4lmtestlubridatemagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqaModelMetricsmodelrnlmenloptrnnetnumDerivparallellypbkrtestpillarpkgconfigplyrpROCprodlimprogressrproxypurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackrecipesreformulasreshape2rlangrpartrpart.plotS7sandwichscalesshapeSparseMsparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdburcautf8vctrsviridisLitewithrzoo

Tutorial
Introduction | Methodology overview | Code | CATE estimation | Constructing the sequence of groupings | Inference
Rendered fromaggTrees-vignette.Rmdusingknitr::rmarkdown

Last update: 2025-02-15
Started: 2023-02-24

Inference
Honesty | Linear Models | Difference in Mean Outcomes | Doubly-Robust Scores
Rendered frominference.Rmdusingknitr::rmarkdown

Last update: 2025-02-15
Started: 2023-09-23