Package: predHCS 0.1.0

predHCS: Point and Interval Prediction for Censored Data under Various Hybrid Censoring Schemes

Implements generalized statistical point prediction and prediction intervals for future failure times under various hybrid censoring schemes. Supported censoring schemes include Type-I, Type-II, Generalized Type-I, Generalized Type-II, Unified, Progressive Type-I, and Progressive Type-II hybrid censoring schemes. Available prediction methods include Best Unbiased Predictor (BUP), Conditional Median Predictor (CMP), Maximum Likelihood Predictor (MLP), equal-tailed classical prediction intervals, Highest Conditional Density (HCD) prediction intervals, and Bayesian prediction intervals. Algorithms accept user-defined continuous probability density functions, cumulative distribution functions, quantile functions, or survival functions along with estimated parameter values. Methodological foundations are based on Balakrishnan, Cramer, and Kundu (2023, ISBN:978-0123983879), Shafay and Balakrishnan (2012) <doi:10.1080/03610918.2011.579367> for Type-I hybrid censoring, Balakrishnan and Shafay (2012) <doi:10.1080/03610926.2010.543300> for Type-II hybrid censoring, Shafay (2017) <doi:10.1080/03610926.2016.1200093> for Generalized Type-I hybrid censoring, Shafay (2016) <doi:10.1080/00949655.2015.1096361> for Generalized Type-II hybrid censoring, Mohie El-Din, Nagy, and Shafay (2017) <doi:10.18576/jsap/060113> for Unified hybrid censoring, Ebrahimi (1992) <doi:10.1109/24.126685>, Valiollahi, Asgharzadeh, and Kundu (2017) <doi:10.1214/15-BJPS302>, and Asgharzadeh, Valiollahi, and Kundu (2015) <doi:10.1080/00949655.2013.848451>.

Authors:Shikhar Tyagi [aut, cre], Arvind Pandey [aut], Bhupendra Singh [aut], Vrijesh Tripathi [aut]