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]

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hettx.pdf |hettx.html
hettx/json (API)
NEWS

# 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:

5.40 score 10 stars 25 scripts 425 downloads 44 exports 31 dependencies

Last updated from:f2e8cd2e28. Checks:8 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK211
source / vignettesOK241
linux-release-x86_64OK180
macos-devel-arm64OK181
macos-release-arm64OK176
windows-develOK148
windows-releaseOK134
wasm-releaseOK122

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

Rendered fromdetect_idiosyncratic_vignette.Rmdusingknitr::rmarkdownon Mar 26 2026.

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

Tutorial on Systematic Treatment Detection

Rendered fromestimate_systematic_vignette.Rmdusingknitr::rmarkdownon Mar 26 2026.

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