gpcihybridIIEM: Generalized Process Capability Indices via EM for Hybrid Type-II
Data
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 Hybrid Type-II censored data (Childs
et al. (2003) <doi:10.1007/BF02517803>; Balakrishnan and Kundu (2013)
<doi:10.1002/nav.21545>) using the 'UniCensorEM' package and computes
Generalized Process Capability Indices (GPCIs). 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, 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.
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