ExtendedABSurvTDC
0.1.0Survival Analysis using Indicators under Time Dependent Covariates
Overview
Survival analysis is employed to model time-to-event data. This package examines the relationship between survival and one or more predictors, termed as covariates, which can include both treatment variables (e.g., season of birth, represented by indicator functions) and continuous variables. To this end, the Cox-proportional hazard (Cox-PH) model, introduced by Cox in 1972, is a widely applicable and commonly used method for survival analysis. This package enables the estimation of the effect of randomization for the treatment variable to account for potential confounders, providing adjustment when estimating the association with exposure. It accommodates both fixed and time-dependent covariates and computes survival probabilities for lactation periods in dairy animals. The package is built upon the algorithm developed by Klein and Moeschberger (2003) DOI:10.1007/b97377.
Install
Health
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 100%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
- Cyclomatic complexity
- 20.0 median / 22 max
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
3 3 exported
Complexity
14.3 avg / 22 max
Call network
3 nodes / 0 edges
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.0Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-10-01
- Total releases
- 1 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.5.0
- Download size
- 11 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet