R Observatory: Metrics for CoxAIPW version 0.0.3

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

CoxAIPW

0.0.3Package record

Doubly Robust Inference for Cox Marginal Structural Model with Informative Censoring

0packages depend

2.7Kdownloads / year

–test coverage

13/13flavors OK

Overview

About

Maintained by Jiyu LuoCRAN page ↗GitHub ↗Issues ↗

Doubly robust estimation and inference of log hazard ratio under the Cox marginal structural model with informative censoring. An augmented inverse probability weighted estimator that involves 3 working models, one for conditional failure time T, one for conditional censoring time C and one for propensity score. Both models for T and C can depend on both a binary treatment A and additional baseline covariates Z, while the propensity score model only depends on Z. With the help of cross-fitting techniques, achieves the rate-doubly robust property that allows the use of most machine learning or non-parametric methods for all 3 working models, which are not permitted in classic inverse probability weighting or doubly robust estimators. When the proportional hazard assumption is violated, CoxAIPW estimates a causal estimated that is a weighted average of the time-varying log hazard ratio. Reference: Luo, J. (2023). Statistical Robustness - Distributed Linear Regression, Informative Censoring, Causal Inference, and Non-Proportional Hazards [Unpublished doctoral dissertation]. University of California San Diego.; Luo & Xu (2022) doi:10.48550/arXiv.2206.02296; Rava (2021) https://escholarship.org/uc/item/8h1846gs.

Changes in 0.0.3

CoxAIPW function now includes an additional crossFit parameter, which can be turned to FALSE to stop cross-fitting when all nuisance function estimators are root-n.
CoxAIPW function now includes an additional augmentation parameter in replacement of the previous 'random_censoring' parameter. Augmentation with respect to either propensity score, censoring or both can be selected.
Under non-proportional hazard model, CoxAIPW estimates a causal estimated that is a weighted average of the time-varying log hazard ratio. It also outputs x and y values for producing a smoothed plot of the time-varying log hazard ratio.
Update reference articles in the DESCRIPTION.

---

From NEWS.md in the release

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("CoxAIPW", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("charlesluo1002/CoxAIPW")

Development version from charlesluo1002/CoxAIPW. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-coxaipw

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-CoxAIPW

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-CoxAIPW

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

13OK

CRAN check page ↗

Slowest check: 2.1 min · r-oldrel-windows-x86_64

Status changes

OK13 OK2026-08-05
NOTE12 OK, 1 NOTE2026-08-01
OK13 OK2026-06-09
ERROR12 OK, 1 ERROR2026-06-08
OK14 OK2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.0.3

READMEYes · 11 wordsVignettesNoNEWSYes · 100% structured · mentions 0.0.3Citation fileNoWebsitenot declared in DESCRIPTION

Help pages: 1
Examples that run: 100%1 run in a plain check
Exported functions with examples: 100%
Documented parameters: 100%
Return-value docs: 100%
Help pages citing references: 0%

How these are measured

Downloads

2.7K

CRAN downloads in the past year

Rank #13,784 · ~7/day · ~227/mo

Releases R versions

Daily download trend is not available in this view yet.

40830 days

83390 days

2.7K1 year

Compare downloads with other packages →

Also on83 r2u7 autocran

How downloads are counted

Repository

charlesluo1002/coxaipw ↗Account: charlesluo1002

1Stars

0Forks

0Open issues

0Open PRs

0Releases

11Commits

5Commit identities

11 commits · Last activity 2023-09-12 · 0% stars, 30d

Responsiveness

not measured

Stars over time

2024-07-27 · 12026-07-07 · 1

How these are measured

PracticesBeta

6 development-tooling and community-health practices detected across 3 families in the upstream repository

Checks run against github.com/charlesluo1002/coxaipw on 2026-10-11.

Continuous integration (3)

GitHub ActionsR CMD check workflowTravis CI (config file)

The R CMD check workflow runs on Linux

CRAN release process (2)

cran-comments.mdCRAN-SUBMISSION

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v3 (2026-09-26)

Dependencies

Declared dependencies

6 external dependencies (excludes base and recommended)

Installing CoxAIPW adds 69 packages (37 with compiled code, 57 MB of source tarballs). Heaviest: stringi 11 MB, bslib 5.6 MB, igraph 4.8 MB.

Depends (0)

none

Imports (7)

survivalrandomForestSRC polspline tidyr ranger pracma gbm

LinkingTo (0)

none

Suggests (0)

none

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

People & History

People (3)

Jiyu Luo

Maintainer since 0.0.1 (2023), Author

Dennis Rava

Author

Ronghui Xu

Author

Releases (3)

Pick two releases to compare their code metrics.

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
R
R 4.4.0 released · 2024-04-24
0.0.3Latest
record
2023-09-20 · current release · diff ↗
0.0.2
record
2023-05-31 · diff ↗
R
R 4.3.0 released · 2023-04-21
0.0.1
record
2023-02-08
R
R 4.2.0 released · 2022-04-22

Package metadata

First published: 2023-02-08
Total releases: 3 / 3 yrs
License: GPL-3 OSI
Download size: 8.4 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("CoxAIPW")

Format

Luo, J., Rava, D., & Xu, R. (2023). CoxAIPW: Doubly Robust Inference for Cox Marginal Structural Model with Informative Censoring (Version 0.0.3) [Computer software]. https://doi.org/10.32614/CRAN.package.CoxAIPW

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for CoxAIPW version 0.0.3 [Data set]. HJJB, LLC. Data release v2026-10-18. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-18, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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