R Observatory: Metrics for coxmnar version 0.1.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

coxmnar

0.1.0Package record

Cox Regression with Missing not at Random Failure Indicators

0packages depend

625downloads / year

81.6%test coverage

13/13flavors OK

Overview

About

Maintained by Shikhar TyagiCRAN page ↗

Implements estimation for the Cox (1972, 1975) doi:10.1111/j.2517-6161.1972.tb00899.x doi:10.1093/biomet/62.2.269 proportional hazards model when the failure indicator (cause of failure) is missing not at random (MNAR), following the two adjusted imputation-based estimating equations of Liu and Liu (2026) doi:10.1007/s11222-026-10857-1. Also provided for comparison are the full-data partial-likelihood estimator of Andersen and Gill (1982) doi:10.1214/aos/1176345976, the complete-case estimator, and the missing-at-random imputation estimator of Liu and Wang (2010, Statistica Sinica, 20, 1125-1142). The probability models for the failure indicator and for the missingness mechanism are estimated jointly by maximum likelihood following Sun, Xie, and Liang (2013) doi:10.1007/s11425-012-4492-x, and a Nadaraya-Watson kernel-smoothed estimator of the missingness propensity is constructed following Qiu, Chen, and Zhou (2015) doi:10.1016/j.spl.2014.12.006. Both an asymptotic (sandwich-type) variance estimator and a nonparametric bootstrap variance estimator are provided. When failure indicators are fully observed the estimators reduce algebraically to the classical Cox partial-likelihood estimator.

Changes in 0.1.0

Initial release implementing Cox proportional hazards regression with missing not at random (MNAR) failure indicators following Liu and Liu (2026) <doi:10.1007/s11222-026-10857-1>.
Implements five estimators: Adjusted Imputation ("ai"), Adjusted Complete ("ac"), Missing at Random ("mar"), Complete-Case ("cc"), and Full-Data ("full").
Joint maximum likelihood estimation of probability models for outcome and missingness mechanism.
Nadaraya-Watson kernel estimation of missingness propensity with coordinate standardization and 5 kernel options.
Asymptotic sandwich variance estimator and nonparametric bootstrap variance estimator.
Convenience function coxmnar_compare() for multi-method comparison.
Data simulation wrapper simulate_coxmnar_data() supporting arbitrary mechanisms.

From NEWS.md in the release

Install

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

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

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

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-coxmnar

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-coxmnar

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-coxmnar

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: 6.1 min · r-oldrel-windows-x86_64

Status changes

OK13 OKNow
OK12 OK2026-08-05
NOTE8 OK, 1 NOTE2026-08-02
OK7 OK2026-08-01

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.1.0

READMEYes · 164 wordsVignettesYes · 1 · dynamicNEWSYes · 67% structured · mentions 0.1.0Citation fileYes · cites a publicationWebsitenot declared in DESCRIPTION

Help pages: 12 (6 internal)includes a package overview page
Examples that run: 100%4 run in a plain check
Exported functions with examples: 100%
Documented parameters: 100%
Return-value docs: 100%
Help pages citing references: 50% with a bibliography file

Vignettes (1)

Introduction to coxmnar HTML

How these are measured

Downloads

625

CRAN downloads in the past year

Rank #21,186 · ~2/day · ~52/mo

Releases R versions

Daily download trend is not available in this view yet.

22130 days

62590 days

6251 year

Compare downloads with other packages →

Also on14 r2u10 autocran

How downloads are counted

Dependencies

Declared dependencies

5 external dependencies (excludes base and recommended)

Installing coxmnar adds 2 packages (1 with compiled code, 1.5 MB of source tarballs). Heaviest: rbibutils 1.1 MB, Rdpack 370 KB.

Depends (1)

R >= 4.1.0

Imports (3)

survivalstatsRdpack

LinkingTo (0)

none

Suggests (4)

testthat knitr rmarkdown covr

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

People & History

People (4)

Shikhar Tyagi

Author, Maintainer since 0.1.0 (2026)

Arvind Pandey

Author

Bhupendra Singh

Author

Vrijesh Tripathi

Author

Releases (1)

0.1.0Latest
record
2026-07-31 · current release
R
R 4.6.0 released · 2026-04-24

Package metadata

First published: 2026-07-31
Total releases: 1 / 1 yrs
License: GPL (>= 3) OSI
Minimum R: ≥ 4.1.0
Download size: 56 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("coxmnar")

The package provides a citation file that builds its entry when R runs it. Run citation() to see it.

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 coxmnar version 0.1.0 [Data set]. HJJB, LLC. Data release v2026-10-22. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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