Generalized Process Capability Indices via EM Algorithm under Progressive Type-II Censoring // Implements the Expectation- Maximization (EM) algorithm of Dempster, Laird, and Rubin (1977) doi:10.1111/j.2517-6161.1977.tb01600.x for parameter estimation under progressive Type-II censored data (Balakrishnan and Aggarwala (2000) doi:10.1007/978-1-4612-1186-0) and computes Generalized Process Capability Indices (GPCIs). Uses the 'UniCensorEM' package for EM estimation. Supports classical and generalized capability indices including Cpy (Maiti et al. (2010) doi:10.1080/16843703.2010.11673233), Cp, Cpk, Cpu, Cpl, Cpm, Cpmk, Spmk (Dey and Saha (2019) doi:10.1007/s41872-019-00081-4), CpTk (Saha et al. (2018) doi:10.1080/21681015.2018.1437793), Cpc, CNpmc (Alotaibi et al. (2022) doi:10.1155/2022/3135264), CNpmkc (Saha et al. (2024) doi:10.1142/S021853932450013X), and CNpk (Saha et al. (2022) doi:10.1080/02664763.2021.1971632). Computes point estimates, bias, mean squared error, risk, Heidelberger and Welch convergence diagnostics, convergence probability, and bootstrap confidence intervals at 90 percent, 95 percent, and 99 percent levels. Accommodates user-defined probability density or mass functions, cumulative distribution functions, and survival functions.