OneShotEM: Efficient eM-Algorithm for One-Shot Device Data Analysis
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>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
stats, graphics, grDevices, utils, methods, numDeriv |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown |
| Published: |
2026-08-21 |
| DOI: |
10.32614/CRAN.package.OneShotEM (may not be active yet) |
| Author: |
Shikhar Tyagi 
[aut, cre],
Arvind Pandey [aut],
Bhupendra Singh [aut],
Vrijesh Tripathi [aut] |
| Maintainer: |
Shikhar Tyagi <shikhar1093tyagi at gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| Language: |
en-US |
| CRAN checks: |
OneShotEM results |
Documentation:
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