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

On CRAN:

13 exports 1.34 score 102 dependencies 3 scripts 341 downloads

Last updated 22 days agofrom:f59504d50a. Checks:OK: 7. Indexed: yes.

Exports:avg_characteristics_rpartbalance_measuresbuild_aggtreecausal_ols_rpartdr_scoresestimate_rpartexpand_dfget_leavesinference_aggtreeleaf_membershipnode_membershipsample_splitsubtree

Dependencies:abindbackportsbootbroomcarcarDatacaretclasscliclockcodetoolscolorspacecowplotcpp11data.tableDerivdiagramDiceKrigingdigestdoBydplyre1071estimatrfansifarverforeachFormulafuturefuture.applygenericsggplot2globalsgluegowergrfgtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlme4lmtestlubridatemagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqaModelMetricsmodelrmunsellnlmenloptrnnetnumDerivparallellypbkrtestpillarpkgconfigplyrpROCprodlimprogressrproxypurrrquantregR6RColorBrewerRcppRcppEigenrecipesreshape2rlangrpartrpart.plotsandwichscalesshapeSparseMSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrzoo