Package: hettx 1.0.1

hettx: Fisherian and Neymanian Methods for Detecting and Measuring Treatment Effect Variation

Implements methods developed by Ding, Feller, and Miratrix (2016) <doi:10.1111/rssb.12124> <doi:10.48550/arXiv.1412.5000>, and Ding, Feller, and Miratrix (2018) <doi:10.1080/01621459.2017.1407322> <doi:10.48550/arXiv.1605.06566> for testing whether there is unexplained variation in treatment effects across observations, and for characterizing the extent of the explained and unexplained variation in treatment effects. The package includes wrapper functions implementing the proposed methods, as well as helper functions for analyzing and visualizing the results of the test.

Authors:Peng Ding [aut], Avi Feller [aut], Ben Fifield [aut, cre], Luke Miratrix [aut]

hettx_1.0.1.tar.gz
hettx_1.0.1.zip(r-4.7-any)hettx_1.0.1.zip(r-4.6-any)hettx_1.0.1.zip(r-4.5-any)
hettx_1.0.1.tgz(r-4.6-any)hettx_1.0.1.tgz(r-4.5-any)
hettx_1.0.1.tar.gz(r-4.7-any)hettx_1.0.1.tar.gz(r-4.6-any)
hettx_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
hettx/json (API)

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

Bug tracker:https://github.com/bfifield/hettx/issues

Datasets:

On CRAN:

Conda:

5.46 score 11 stars 26 scripts 326 downloads 44 exports 31 dependencies

Last updated from:f2e8cd2e28. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK191
source / vignettesOK242
linux-releaseOK185
macos-releaseOK176
macos-oldrelOK190
windows-develOK163
windows-releaseOK122
windows-oldrelOK140
wasm-releaseOK116

Exports:detect_idiosyncraticestimate_systematicget_p_valueget.p.valueKS_statKS.statmake_linear_datamake_quadradic_datamake_randomized_compliance_datmake_randomized_datmake_skew_datamake.linear.datamake.quadradic.datamake.randomized.compliance.datmake.randomized.datmake.skew.dataR2rq_statrq_stat_cond_covrq_stat_uncond_covrq.statrq.stat.cond.covrq.stat.uncond.covSESKS_pool_tSKS_statSKS_stat_covSKS_stat_cov_poolSKS_stat_cov_rqSKS_stat_int_covSKS_stat_int_cov_poolSKS.pool.tSKS.statSKS.stat.covSKS.stat.cov.poolSKS.stat.cov.rqSKS.stat.int.covSKS.stat.int.cov.pooltest_stat_infotest.stat.infovariance_ratio_testvariance.ratio.testWSKS_tWSKS.t

Dependencies:clicodetoolscpp11doParallelfarverforeachgenericsggplot2gluegtableisobanditeratorslabelinglatticelifecycleMASSMatrixMatrixModelsmomentsmvtnormquantregR6RColorBrewerrlangS7scalesSparseMsurvivalvctrsviridisLitewithr

detect_idiosyncratic() Tutorial
Introduction | Unfortunately, we cannot directly release the HSIS data. | SAY MORE ABOUT DGP: what Xs, etc. What is true model? | The illustrative Dataset | Testing for ideosyncratic treatment effect variation | Basic case: no covariate adjustment | Adjusting for covariates | Ideosyncratic variation beyond systematic variation | Testing for ideosyncratic variation. | Comparing the tests | Cautionary Tale: A linear model with no treatment interaction. | A simple variance ratio test | The variety of test statistics
Rendered fromdetect_idiosyncratic_vignette.Rmdusingknitr::rmarkdown

Last update: 2026-02-24
Started: 2019-01-21

Tutorial on Systematic Treatment Detection
Introduction | Basic estimation | OLS adjustment | Model adjustment | Model adjustment + OLS adjustment | Oracle estimator (for simulations and verification of formulae) | Looking at $R^2$ | Comparing estimators | Treatment variation and non-compliance | Estimating the effects | The 2SLS Approach
Rendered fromestimate_systematic_vignette.Rmdusingknitr::rmarkdown

Last update: 2026-02-24
Started: 2019-01-21