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 doi:10.1080/16843703.2010.11673233), Spmk (Dey Saha, 2019 doi:10.1007/s41872-019-00081-4), CpTk (Saha et al., 2019), Cpc (Saha et al., 2022 doi:10.1080/02664763.2021.1971632), CNpmc (Alotaibi et al., 2022 doi:10.1155/2022/3135264), CNpmkc (Saha et al., 2024 doi:10.1142/S021853932450013X), CNpk (Saha et al., 2018 doi:10.1080/21681015.2018.1437793), 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) doi:10.1007/978-1-4612-1186-0, Maiti, Saha Nanda (2010) doi:10.1080/16843703.2010.11673233, Saha, Dey Maiti (2018) doi:10.1080/21681015.2018.1437793, Dey Saha (2019) doi:10.1007/s41872-019-00081-4, Saha, Dey Maiti (2019), Alotaibi, Dey Saha (2022) doi:10.1155/2022/3135264, Saha, Dey Nadarajah (2022) doi:10.1080/02664763.2021.1971632, Saha, Tripathi Dey (2024) doi:10.1142/S021853932450013X.