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 (2022) <doi:10.2139/ssrn.4304256>.

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

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aggTrees.pdf |aggTrees.html✨
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NEWS

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

Peer review:

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

On CRAN:

4.48 score 3 scripts 341 downloads 13 exports 102 dependencies

Last updated 3 months agofrom:f59504d50a. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 26 2024
R-4.5-winOKOct 26 2024
R-4.5-linuxOKOct 26 2024
R-4.4-winOKOct 26 2024
R-4.4-macOKOct 26 2024
R-4.3-winOKOct 26 2024
R-4.3-macOKOct 26 2024

Exports:avg_characteristics_rpartbalance_measuresbuild_aggtreecausal_ols_rpartdr_scoresestimate_rpartexpand_dfget_leavesinference_aggtreeleaf_membershipnode_membershipsample_splitsubtree

Dependencies:abindbackportsbootbroomcarcarDatacaretclasscliclockcodetoolscolorspacecowplotcpp11data.tableDerivdiagramDiceKrigingdigestdoBydplyre1071estimatrfansifarverforeachFormulafuturefuture.applygenericsggplot2globalsgluegowergrfgtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlme4lmtestlubridatemagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqaModelMetricsmodelrmunsellnlmenloptrnnetnumDerivparallellypbkrtestpillarpkgconfigplyrpROCprodlimprogressrproxypurrrquantregR6RColorBrewerRcppRcppEigenrecipesreshape2rlangrpartrpart.plotsandwichscalesshapeSparseMSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrzoo

Inference

Rendered frominference.Rmdusingknitr::rmarkdownon Oct 26 2024.

Last update: 2023-09-25
Started: 2023-09-23

Short Tutorial

Rendered fromaggTrees-vignette.Rmdusingknitr::rmarkdownon Oct 26 2024.

Last update: 2024-08-26
Started: 2023-02-24