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OneShotEM  

Efficient eM-Algorithm for One-Shot Device Data Analysis
View on CRAN: Click here


Download and install OneShotEM package within the R console
Install from CRAN:
install.packages("OneShotEM")

Install from Github:
library("remotes")
install_github("cran/OneShotEM")

Install by package version:
library("remotes")
install_version("OneShotEM", "0.1.0")



Attach the package and use:
library("OneShotEM")
Maintained by
Shikhar Tyagi
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-08-21
Latest Update: 2026-08-21
Description:
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>.
How to cite:
Shikhar Tyagi (2026). OneShotEM: Efficient eM-Algorithm for One-Shot Device Data Analysis. R package version 0.1.0, https://cran.r-project.org/web/packages/OneShotEM. Accessed 04 Oct. 2026.
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