Generalized Process Capability Indices under Progressive Type-II Censoring

Provides a comprehensive generalized framework for parameter estimation and Generalized Process Capability Indices (GPCIs) under Progressive Type-II censored data using the MleCensoR package. Accepts user-supplied probability density functions (PDF/PMF), cumulative distribution functions (CDF), and survival functions (SF). Computes classical and generalized capability indices including Cpy (Maiti et al., 2010 ), Spmk (Dey & Saha, 2019 ), CpTk (Saha et al., 2019), Cpc (Saha et al., 2022 ), CNpmc (Alotaibi et al., 2022 ), CNpmkc (Saha et al., 2024 ), CNpk (Saha et al., 2018 ), and Vannman's Cp(u,v) family. Evaluates parametric and non-parametric bootstrap confidence intervals at 90%, 95%, and 99% levels of significance. Computes Standard Errors, Mean Squared Error (MSE), Bias, and Coverage Probabilities for model parameters and capability indices. References: Balakrishnan & Aggarwala (2000) , Maiti, Saha & Nanda (2010) , Saha, Dey & Maiti (2018) , Dey & Saha (2019) , Saha, Dey & Maiti (2019), Alotaibi, Dey & Saha (2022) , Saha, Dey & Nadarajah (2022) , Saha, Tripathi & Dey (2024) .