Package: PDRobust 0.3.8

PDRobust: Robust Longitudinal Effects Under Truncation by Death

Implements principal-stratification methods for estimating time-specific and pooled heterogeneous treatment effects in longitudinal studies where outcomes may be truncated by death. Supports continuous and binary outcomes, explicit data validation and standardization, and covariate-dependent treatment effects. Fits propensity-score, principal-score, and outcome models and provides subject-level bootstrap inference, covariate-balance diagnostics, principal-stratum summaries, treatment-group-specific survival odds ratios, and outcome-noise sensitivity analysis. Methodological background is provided in <doi:10.48550/arXiv.2608.06654>.

Authors:Huan Wang [aut, cre], Yilin Zhang [aut]

PDRobust_0.3.8.tar.gz
PDRobust_0.3.8.zip(r-4.7-any)PDRobust_0.3.8.zip(r-4.6-any)PDRobust_0.3.8.zip(r-4.5-any)
PDRobust_0.3.8.tgz(r-4.6-any)PDRobust_0.3.8.tgz(r-4.5-any)
PDRobust_0.3.8.tar.gz(r-4.7-any)PDRobust_0.3.8.tar.gz(r-4.6-any)
PDRobust_0.3.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
PDRobust/json (API)

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

Bug tracker:https://github.com/whhuan/pd_robust/issues

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

Datasets:

On CRAN:

Conda:

5.24 score 13 exports 25 dependencies

Last updated from:63613bb35c. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK138
source / vignettesOK201
linux-releaseOK116
macos-releaseOK210
macos-oldrelOK203
windows-develOK105
windows-releaseOK93
windows-oldrelOK105
wasm-releaseOK103

Exports:DataCheckDataStandardHTEAllTHTESepTMappingORCIOutPredPrinPredPrinSDiagPSDiagPSPredQRSA

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglatticelifecycleMASSMatrixMatrixModelsquantregR6RColorBrewerrlangrootSolveS7scalesSparseMsurvivalvctrsviridisLitewithr

Diagnostics, profiling, and sensitivity analysis
Introduction | Propensity score, covariate balance | Principal score, covariate-specific balance statistic | Outcome-noise sensitivity analysis | Principal-stratum summaries | Treatment-specific survial odds ratios
Rendered fromdiagnostics-profiling-sensitivity.Rmdusingknitr::rmarkdown

Last update: 2026-09-17
Started: 2026-08-02

Treatment coding and interpretation
Choose treatment coding before analysis | Report the intended effect direction | Interpret the working effect model | Assumptions, numerical safeguards, and scope
Rendered frommethod-and-coding.Rmdusingknitr::rmarkdown

Last update: 2026-09-17
Started: 2026-09-13

Detailed Function Presentation
PDRobust | 1. Load the built-in package data | 2. Define roles and analysis settings with Mapping() | 3. Validate the raw data with DataCheck() | 4. Standardize the panel with DataStandard() | Imperfect dataset | 5.1 Prediction functions and Diagnostics | Propensity score model | Principal score model | Outcome model | Principal-stratum profiling with QR() | Treatment-group odds ratios | 5.2 Heterogeneous treatment effect
Rendered fromFunctions.Rmdusingknitr::rmarkdown

Last update: 2026-09-13
Started: 2026-07-31

Data requirements and standardization
Introduction | Mapping raw columns and times | Validation without modification | Standardization and audit attributes
Rendered fromdata-requirements.Rmdusingknitr::rmarkdown

Last update: 2026-09-13
Started: 2026-07-31

Details for function DataCheck
Columns in the itemized checks
Rendered fromDetails-for-Datacheck.Rmdusingknitr::rmarkdown

Last update: 2026-09-13
Started: 2026-08-01

Estimate the heterogeneous treatment effect
Introduction | Time-varying heterogeneous treatment effects | Pooled heterogeneous treatment effects
Rendered fromhte.Rmdusingknitr::rmarkdown

Last update: 2026-09-13
Started: 2026-08-02

Prediction models
Introduction | Propensity score model | Principal score model | Outcome prediction model
Rendered fromprediction-models.Rmdusingknitr::rmarkdown

Last update: 2026-08-02
Started: 2026-07-31