Package: tidyhte 1.0.4

Drew Dimmery

tidyhte: Tidy Estimation of Heterogeneous Treatment Effects

Estimates heterogeneous treatment effects using tidy semantics on experimental or observational data. Methods are based on the doubly-robust learner of Kennedy (2023) <doi:10.1214/23-EJS2157>. You provide a simple recipe for what machine learning algorithms to use in estimating the nuisance functions and 'tidyhte' will take care of cross-validation, estimation, model selection, diagnostics and construction of relevant quantities of interest about the variability of treatment effects.

Authors:Drew Dimmery [aut, cre, cph]

tidyhte_1.0.4.tar.gz
tidyhte_1.0.4.zip(r-4.7)tidyhte_1.0.4.zip(r-4.6)tidyhte_1.0.4.zip(r-4.5)
tidyhte_1.0.4.tgz(r-4.6-any)tidyhte_1.0.4.tgz(r-4.5-any)
tidyhte_1.0.4.tar.gz(r-4.7-any)tidyhte_1.0.4.tar.gz(r-4.6-any)
tidyhte_1.0.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
tidyhte/json (API)
NEWS

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

Bug tracker:https://github.com/ddimmery/tidyhte/issues

On CRAN:

Conda:

6.16 score 16 stars 15 scripts 692 downloads 32 exports 36 dependencies

Last updated from:ee927ee94b. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK159
source / vignettesOK394
linux-release-x86_64OK217
macos-release-arm64OK154
macos-oldrel-arm64OK176
windows-develOK117
windows-releaseOK116
windows-oldrelOK130
wasm-releaseOK129

Exports:add_effect_diagnosticadd_effect_modeladd_known_propensity_scoreadd_moderatoradd_outcome_diagnosticadd_outcome_modeladd_propensity_diagnosticadd_propensity_score_modeladd_vimpattach_configbasic_configConstant_cfgconstruct_pseudo_outcomesDiagnostics_cfgestimate_diagnosticestimate_QoIHTE_cfgKernelSmooth_cfgKnown_cfgmake_splitsMCATE_cfgModel_cfgModel_dataPCATE_cfgproduce_plugin_estimatesQoI_cfgremove_vimpSL.glmnet.interactionSLEnsemble_cfgSLLearner_cfgStratified_cfgVIMP_cfg

Dependencies:backportsbitopscaToolscheckmateclicodetoolscrayoncvAUCdata.tabledplyrforeachgamgenericsgluegplotsgtoolshmsiteratorsKernSmoothlifecyclemagrittrnnlspillarpkgconfigprettyunitsprogresspurrrR6rlangROCRSuperLearnertibbletidyselectutf8vctrswithr

HTE Analysis in an Experiment

Drew Dimmery ([email protected])

Rendered fromexperimental_analysis.Rmdusingknitr::rmarkdownon May 09 2026.

Last update: 2025-10-09
Started: 2021-10-25

HTE Analysis on Observational Data

Drew Dimmery ([email protected])

Rendered fromobservational_analysis.Rmdusingknitr::rmarkdownon May 09 2026.

Last update: 2025-10-09
Started: 2021-10-25

Methodological Details

Drew Dimmery ([email protected]), Edward Kennedy ([email protected])

Rendered frommethodological_details.Rmdusingknitr::rmarkdownon May 09 2026.

Last update: 2025-10-09
Started: 2021-11-18

Readme and manuals

Help Manual

Help pageTopics
Add an additional diagnostic to the effect modeladd_effect_diagnostic
Add an additional model to the joint effect ensembleadd_effect_model
Uses a known propensity scoreadd_known_propensity_score
Adds moderators to the configurationadd_moderator
Add an additional diagnostic to the outcome modeladd_outcome_diagnostic
Add an additional model to the outcome ensembleadd_outcome_model
Add an additional diagnostic to the propensity scoreadd_propensity_diagnostic
Add an additional model to the propensity score ensembleadd_propensity_score_model
Adds variable importance informationadd_vimp
Attach an 'HTE_cfg' to a dataframeattach_config
Create a basic config for HTE estimationbasic_config
Configuration of a Constant EstimatorConstant_cfg
Construct Pseudo-outcomesconstruct_pseudo_outcomes
Configuration of Model DiagnosticsDiagnostics_cfg
Estimate Quantities of Interestestimate_QoI
Configuration of Quantities of InterestHTE_cfg
Configuration for a Kernel SmootherKernelSmooth_cfg
Configuration of Known ModelKnown_cfg
Define splits for cross-fittingmake_splits
Configuration of Marginal CATEsMCATE_cfg
Base Class of Model ConfigurationsModel_cfg
R6 class to represent data to be used in estimating a modelModel_data
Prediction for an SL.glmnet objectpredict.SL.glmnet.interaction
Estimate models of nuisance functionsproduce_plugin_estimates
Configuration of Quantities of InterestQoI_cfg
Removes variable importance informationremove_vimp
Elastic net regression with pairwise interactionsSL.glmnet.interaction
Configuration for a SuperLearner EnsembleSLEnsemble_cfg
Configuration of SuperLearner SubmodelSLLearner_cfg
Configuration for a Stratification EstimatorStratified_cfg
Configuration of Variable ImportanceVIMP_cfg