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  "Title": "Importance Sampling Estimation of Generalized Process Capability\nIndices",
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  "Description": "Provides a comprehensive generalized framework for\nparameter estimation and Generalized Process Capability Indices\n(GPCIs) under uncensored data using Importance Sampling\n(ImpSam). Supports user-supplied probability density functions\n(PDF/PMF), cumulative distribution functions (CDF), and\nsurvival functions (SF). Computes classical and generalized\ncapability indices including Cpy (Maiti et al., 2010\n<doi:10.1080/16843703.2010.11673233>), Spmk (Dey & Saha, 2019\n<doi:10.1080/00949655.2019.1671980>), CpTk (Saha et al., 2019),\nCpc (Saha et al., 2022 <doi:10.1080/02664763.2021.1971632>),\nCNpmc (Alotaibi et al., 2022 <doi:10.1155/2022/3135264>),\nCNpmkc (Saha et al., 2024 <doi:10.1142/S021853932450013X>),\nCNpk (Saha et al., 2018 <doi:10.1080/21681015.2018.1437793>),\nand Vannman's Cp(u,v) family. Generates parameter and GPCI MCMC\nchains via Sampling Importance Resampling (SIR) after burn-in\nand thinning. Provides point estimates, bias, MSE, risk values,\nHighest Posterior Density (HPD) intervals at 90, 95, and 99\npercent levels of significance, Heidelberger and Welch MCMC\nconvergence diagnostic, and convergence probability.",
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    {
      "page": "capability",
      "title": "Compute Process Capability Indices (GPCIs)",
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        "capability"
      ]
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      "title": "Theoretical Moments Computation for Process Distribution",
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      "title": "Define a Process Distribution",
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      "title": "Standard Pre-defined Exponential Distribution",
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      "title": "Uncensored Data Parameter Estimation via Maximum Likelihood",
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      "title": "High-Level User Interface Function for Importance Sampling GPCIs",
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    },
    {
      "page": "impsam_gpci",
      "title": "Importance Sampling MCMC Chain Generator for Process Capability Indices",
      "topics": [
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      "title": "Plot Method for gpciImpSam Objects",
      "topics": [
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      "title": "Summary and Diagnostics for gpciImpSam Objects",
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