Efficient eM-Algorithm for One-Shot Device Data Analysis
Overview
Implements the simple and efficient Expectation-Maximization (eM) algorithm proposed by Zhu, Li, Li, and Balakrishnan (2026) doi:10.1080/03610918.2025.2515193 for parameter estimation in one-shot device accelerated life testing (ALT) data. Unlike traditional EM algorithms that impute exact failure times, this method treats failure counts between inspection intervals as missing data, resulting in faster convergence and enhanced numerical stability. Supports Exponential, Weibull, Lognormal, Gamma, and custom user-defined lifetime distributions under log-linear stress models. Standard errors, confidence intervals, model selection statistics (AIC, BIC, AICc, HQIC), residual diagnostics, and visualization tools are provided. References: Balakrishnan and Ling (2012) doi:10.1016/j.csda.2011.09.010, Fan, Balakrishnan, and Chang (2009) doi:10.1080/00949650802142592.
Install
Health
- OK13 OKNow
- OK7 OK2026-08-22
Documentation
- Help pages
- 17 (1 internal)includes a package overview page
- Examples that run
- 100%6 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 24%
Downloads
Dependencies
Code & Tests
Datasets
People & History
- 0.1.0Latest2026-08-21 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-08-21
- Total releases
- 1 / 1 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 4.0.0
- Bundled data
- 0.2 KB / 1 file
- Download size
- 40 KB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("OneShotEM")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.
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.