Package: qte 2.0.0

qte: Quantile Treatment Effects

Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main cases covered are (i) treatment is randomly assigned, (ii) treatment is as good as randomly assigned after conditioning on covariates (selection on observables) using the methods of Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, and (iii) identification is based on a Difference in Differences assumption, with support for several varieties including Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x>, Callaway and Li (2019) <doi:10.3982/QE935>, and Callaway, Li, and Oka (2018) <doi:10.1016/j.jeconom.2018.06.008>. Version 2.0 adds a unified staggered treatment adoption API (built on 'ptetools') for all DiD-based estimators, as well as a new lagged-outcome unconfoundedness estimator ('lou_qtt').

Authors:Brantly Callaway [aut, cre]

qte_2.0.0.tar.gz
qte_2.0.0.zip(r-4.7-any)qte_2.0.0.zip(r-4.6-any)qte_2.0.0.zip(r-4.5-any)
qte_2.0.0.tgz(r-4.6-any)qte_2.0.0.tgz(r-4.5-any)
qte_2.0.0.tar.gz(r-4.7-any)qte_2.0.0.tar.gz(r-4.6-any)
qte_2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
qte/json (API)

# Install 'qte' in R:
install.packages('qte', repos = c('https://bcallaway11.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/bcallaway11/qte/issues

Pkgdown/docs site:https://bcallaway11.github.io

Datasets:

On CRAN:

Conda:

quarto

6.63 score 12 stars 79 scripts 560 downloads 23 exports 50 dependencies

Last updated from:33d50f5c13. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK188
source / vignettesOK360
linux-releaseOK193
macos-releaseOK258
macos-oldrelOK280
windows-develOK196
windows-releaseOK173
windows-oldrelOK194
wasm-releaseOK117

Exports:ci.qteci.qtetcicCiCcic_gtddidddid_gtddid2ggqtelou_qttmdidMDiDmdid_gtpanel_qttpanel_qtt_gtpanel.qtetpanelize.dataqdidQDiDqdid_gtQTEQTEparamsunc_qte

Dependencies:BHbigmemorybigmemory.sriBMiscclicpp11data.tabledplyrDRDIDfarverfastglmFormulaformula.toolsgenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixMatrixModelsoperator.toolspbapplypillarpkgconfigptetoolsquantregR6RColorBrewerRcppRcppArmadilloRcppEigenrlangS7scalesSparseMsplines2survivaltibbletidyselecttrustutf8uuidvctrsviridisLitewithr

Panel Data Estimators for Quantile Treatment Effects
Overview | Change in Changes — cic() | Quantile Difference-in-Differences — qdid() | Panel QTT — panel_qtt() | Distributional DiD — ddid() | Mean Difference-in-Differences — mdid() | Lagged-Outcome Unconfoundedness — lou_qtt() | Summary | References
Rendered frompanel-estimators.qmdusingquarto::html

Last update: 2026-07-23
Started: 2026-05-25

Quantile Treatment Effects in R
What are Quantile Treatment Effects? | Random assignment | Unconfoundedness | QTT under unconfoundedness | Reading the output | References
Rendered fromintro.qmdusingquarto::html

Last update: 2026-07-23
Started: 2026-05-23

Staggered Treatment Adoption with the qte Package
Introduction | QTT curve estimation | Overall QTT curve | Dynamic QTT — event-study by quantile | ATT and event-study summary | Comparing estimators | Repeated cross sections | Inference notes | References
Rendered fromstaggered-adoption.qmdusingquarto::html

Last update: 2026-07-23
Started: 2026-05-23