stagelife: Random Number Generation and Inferences for Two-Stage
Life-Testing Data
Provides random number generation, estimation, and hypothesis
testing for two-stage life-testing experiments in which units begin on
a baseline stress and, at a fixed or random change point, some or all
surviving units move to an elevated stress ("stage life testing"),
following the cumulative-exposure model of Laumen and Cramer (2019),
"Stage life testing", <doi:10.1002/nav.21874>. Twelve baseline lifetime
distributions are supplied (Weibull, Power Lindley, Exponentiated Lindley,
Exponentiated Exponential, Marshall-Olkin Exponential, Lomax,
Exponentiated Teissier, Log-Logistic, Chen, Log-normal, Gamma, and Power
Muth/Power Teissier) together with three parameter-sharing cases
(common, shape1, shape11). The package implements data simulation
under both the fixed-proportion (typeP) and fixed-count (typeM) rules
and the simple-step-stress special case, maximum likelihood estimation
using a hybrid Nelder-Mead and BFGS optimisation routine, asymptotic
(Wald) and non-parametric/parametric bootstrap confidence
intervals (including log-scale variants that guarantee a positive
interval), a stage-1-observation planning probability, Kolmogorov-Smirnov
goodness-of-fit testing with a parametric-bootstrap correction for
estimated parameters, likelihood-ratio and information criteria for
nested/non-nested model comparison, and diagnostic plots comparing
fitted and empirical distributions, including the cumulative
distribution function (CDF), survival function, probability-probability
(P-P), and quantile-quantile (Q-Q) plots.
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=stagelife
to link to this page.