Generalized Process Capability Indices via EM for Hybrid Type-II Data

Implements the Expectation-Maximization (EM) algorithm of Dempster, Laird, and Rubin (1977) for parameter estimation under Hybrid Type-II censored data (Childs et al. (2003) ; Balakrishnan and Kundu (2013) ) using the 'UniCensorEM' package and computes Generalized Process Capability Indices (GPCIs). 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, Bayesian MCMC sampling chains, goodness-of-fit testing via 'gofPHCS', 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.