Generalized Process Capability Indices via EM Algorithm under Progressive Type-II Censoring

Implements the Expectation-Maximization (EM) algorithm of Dempster, Laird, and Rubin (1977) for parameter estimation under progressive Type-II censored data (Balakrishnan and Aggarwala (2000) ) 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) ), Cp, Cpk, Cpu, Cpl, Cpm, Cpmk, Spmk (Dey and Saha (2019) ), CpTk (Saha et al. (2018) ), Cpc, CNpmc (Alotaibi et al. (2022) ), CNpmkc (Saha et al. (2024) ), and CNpk (Saha et al. (2022) ). 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.