Package: GenericML 0.2.3

GenericML: Generic Machine Learning Inference

Generic Machine Learning Inference on heterogeneous treatment effects in randomized experiments as proposed in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) <arxiv:1712.04802>. This package's workhorse is the 'mlr3' framework of Lang et al. (2019) <doi:10.21105/joss.01903>, which enables the specification of a wide variety of machine learners. The main functionality, GenericML(), runs Algorithm 1 in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) <arxiv:1712.04802> for a suite of user-specified machine learners. All steps in the algorithm are customizable via setup functions. Methods for printing and plotting are available for objects returned by GenericML(). Parallel computing is supported.

Authors:Max Welz [aut, cre], Andreas Alfons [aut], Mert Demirer [aut], Victor Chernozhukov [aut]

GenericML_0.2.3.tar.gz
GenericML_0.2.3.zip(r-4.5)GenericML_0.2.3.zip(r-4.4)GenericML_0.2.3.zip(r-4.3)
GenericML_0.2.3.tgz(r-4.4-any)GenericML_0.2.3.tgz(r-4.3-any)
GenericML_0.2.3.tar.gz(r-4.5-noble)GenericML_0.2.3.tar.gz(r-4.4-noble)
GenericML_0.2.3.tgz(r-4.4-emscripten)GenericML_0.2.3.tgz(r-4.3-emscripten)
GenericML.pdf |GenericML.html✨
GenericML/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/mwelz/genericml/issues

On CRAN:

4.51 score 64 stars 8 scripts 319 downloads 23 exports 54 dependencies

Last updated 2 years agofrom:a33be27d4e. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 18 2024
R-4.5-winOKNov 18 2024
R-4.5-linuxOKNov 18 2024
R-4.4-winOKNov 18 2024
R-4.4-macOKNov 18 2024
R-4.3-winOKNov 18 2024
R-4.3-macOKNov 18 2024

Exports:BLPCLANGATESGenericMLGenericML_combineGenericML_singleget_bestget_BLPget_CLANget_GATESheterogeneity_CLANlambda_parametersMedpropensity_scoreproxy_BCAproxy_CATEquantile_groupsetup_diffsetup_plotsetup_stratifysetup_vcovsetup_X1TrueIfUnix

Dependencies:abindbackportscheckmateclicodetoolscolorspacedata.tabledigestevaluatefansifarverfuturefuture.applyggplot2globalsgluegtableisobandlabelinglatticelgrlifecyclelistenvlmtestmagrittrMASSMatrixmgcvmlbenchmlr3mlr3learnersmlr3measuresmlr3miscmunsellnlmepalmerpenguinsparadoxparallellypillarpkgconfigPRROCR6RColorBrewerrlangsandwichscalessplitstackshapetibbleutf8uuidvctrsviridisLitewithrzoo

Readme and manuals

Help Manual

Help pageTopics
Performs BLP regressionBLP
Performs CLANCLAN
Performs GATES regressionGATES
Generic Machine Learning InferenceGenericML
Combine several GenericML objectsGenericML_combine
Single iteration of the GenericML algorithmGenericML_single
Accessor function for the best learner estimatesget_best
Accessor function for the BLP generic target estimatesget_BLP
Accessor function for the CLAN generic target estimatesget_CLAN
Accessor function for the GATES generic target estimatesget_GATES
Evaluate treatment effect heterogeneity along CLAN variablesheterogeneity_CLAN
Estimate the two lambda parameterslambda_parameters
Calculate lower and upper medianMed
Plot method for a '"GenericML"' objectplot.GenericML
Print method for a '"BLP_info"' objectprint.BLP_info
Print method for a '"CLAN_info"' objectprint.CLAN_info
Print method for a '"GATES_info"' objectprint.GATES_info
Print method for a 'GenericML' objectprint.GenericML
Print method for a '"heterogeneity_CLAN"' objectprint.heterogeneity_CLAN
Propensity score estimationpropensity_score
Baseline Conditional Averageproxy_BCA
Conditional Average Treatment Effectproxy_CATE
Partition a vector into quantile groupsquantile_group
Setup function for 'diff' argumentssetup_diff
Set up information for a 'GenericML()' plotsetup_plot
Setup function for stratified samplingsetup_stratify
Setup function for 'vcov_control' argumentssetup_vcov
Setup function controlling the matrix X_1 in the BLP or GATES regressionsetup_X1
Check if user's OS is a Unix systemTrueIfUnix