A comprehensive framework for computing, estimating, and validating
Generalized Process Capability Indices (GPCIs) under interval-censored data.
Supports user-supplied probability density functions (PDF/PMF), cumulative
distribution functions (CDF), and survival functions (SF). Parameter estimation
is performed using Maximum Likelihood Estimation for interval-censored data via
the MleCensoR package. Computes classical and generalized capability indices
including Cpy (Maiti et al., 2010), Spmk (Dey & Saha, 2019), CpTk (Saha et al.,
2019), Cpc (Saha et al., 2022), CNpmc (Alotaibi et al., 2022), CNpmkc (Saha et
al., 2024), CNpk (Saha et al., 2018), and Vannman's Cp(u,v) family. Provides
parametric and non-parametric bootstrap confidence intervals at 90%, 95%, and 99%
confidence levels using percentile, normal, basic, BCa, BCp, and studentized
bootstrap methods. Computes standard errors, mean squared errors, and coverage
probabilities for both distribution parameters and capability indices.
References:
Maiti, Saha & Nanda (2010)