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      "version": "11.6.3",
      "date": "2025-11-28"
    },
    {
      "version": "11.6.4",
      "date": "2026-01-10"
    },
    {
      "version": "11.6.5",
      "date": "2026-03-11"
    },
    {
      "version": "12.0",
      "date": "2026-04-10"
    },
    {
      "version": "12.1",
      "date": "2026-06-05"
    },
    {
      "version": "13.0",
      "date": "2026-06-30"
    }
  ],
  "_exports": [
    "Co.LPM",
    "Co.LPM_nD",
    "Co.UPM",
    "Co.UPM_nD",
    "D.LPM",
    "D.UPM",
    "DPM_nD",
    "dy.d_",
    "dy.dx",
    "LPM",
    "LPM.ratio",
    "LPM.VaR",
    "NNS.ANOVA",
    "NNS.ARMA",
    "NNS.ARMA.optim",
    "NNS.boost",
    "NNS.caus",
    "NNS.CDF",
    "NNS.copula",
    "NNS.dep",
    "NNS.diff",
    "NNS.distance",
    "NNS.FSD",
    "NNS.FSD.uni",
    "NNS.gravity",
    "NNS.MC",
    "NNS.meboot",
    "NNS.mode",
    "NNS.moments",
    "NNS.norm",
    "NNS.part",
    "NNS.reg",
    "NNS.rescale",
    "NNS.SD.cluster",
    "NNS.SD.efficient.set",
    "NNS.seas",
    "NNS.SS",
    "NNS.SSD",
    "NNS.SSD.uni",
    "NNS.stack",
    "NNS.TSD",
    "NNS.TSD.uni",
    "NNS.VAR",
    "PM.matrix",
    "UPM",
    "UPM.ratio",
    "UPM.VaR"
  ],
  "_help": [
    {
      "page": "Co.LPM",
      "title": "Co‑Lower Partial Moment",
      "topics": [
        "Co.LPM"
      ]
    },
    {
      "page": "Co.LPM_nD",
      "title": "Co‑Lower Partial Moment nD",
      "topics": [
        "Co.LPM_nD"
      ]
    },
    {
      "page": "Co.UPM",
      "title": "Co‑Upper Partial Moment",
      "topics": [
        "Co.UPM"
      ]
    },
    {
      "page": "Co.UPM_nD",
      "title": "Co‑Upper Partial Moment nD",
      "topics": [
        "Co.UPM_nD"
      ]
    },
    {
      "page": "D.LPM",
      "title": "Divergent‑Lower Partial Moment",
      "topics": [
        "D.LPM"
      ]
    },
    {
      "page": "D.UPM",
      "title": "Divergent‑Upper Partial Moment",
      "topics": [
        "D.UPM"
      ]
    },
    {
      "page": "DPM_nD",
      "title": "Divergent Partial Moment nD",
      "topics": [
        "DPM_nD"
      ]
    },
    {
      "page": "dy.d_",
      "title": "Partial Derivative dy/d_[wrt]",
      "topics": [
        "dy.d_"
      ]
    },
    {
      "page": "dy.dx",
      "title": "Partial Derivative dy/dx",
      "topics": [
        "dy.dx"
      ]
    },
    {
      "page": "LPM",
      "title": "Lower Partial Moment",
      "topics": [
        "LPM"
      ]
    },
    {
      "page": "LPM.ratio",
      "title": "Lower Partial Moment Ratio",
      "topics": [
        "LPM.ratio"
      ]
    },
    {
      "page": "LPM.VaR",
      "title": "LPM VaR",
      "topics": [
        "LPM.VaR"
      ]
    },
    {
      "page": "NNS_bin",
      "title": "Fast binning of numeric vector into equidistant bins",
      "topics": [
        "NNS_bin"
      ]
    },
    {
      "page": "NNS.ANOVA",
      "title": "NNS ANOVA: Nonparametric Analysis of Variance",
      "topics": [
        "NNS.ANOVA"
      ]
    },
    {
      "page": "NNS.ARMA",
      "title": "NNS ARMA",
      "topics": [
        "NNS.ARMA"
      ]
    },
    {
      "page": "NNS.ARMA.optim",
      "title": "NNS ARMA Optimizer",
      "topics": [
        "NNS.ARMA.optim"
      ]
    },
    {
      "page": "NNS.boost",
      "title": "NNS Boost",
      "topics": [
        "NNS.boost"
      ]
    },
    {
      "page": "NNS.caus",
      "title": "NNS Causation",
      "topics": [
        "NNS.caus"
      ]
    },
    {
      "page": "NNS.CDF",
      "title": "NNS CDF",
      "topics": [
        "NNS.CDF"
      ]
    },
    {
      "page": "NNS.copula",
      "title": "NNS Co-Partial Moments Higher Dimension Dependence",
      "topics": [
        "NNS.copula"
      ]
    },
    {
      "page": "NNS.dep",
      "title": "NNS Dependence",
      "topics": [
        "NNS.dep"
      ]
    },
    {
      "page": "NNS.diff",
      "title": "NNS Numerical Differentiation",
      "topics": [
        "NNS.diff"
      ]
    },
    {
      "page": "NNS.distance",
      "title": "NNS Distance",
      "topics": [
        "NNS.distance"
      ]
    },
    {
      "page": "NNS.FSD",
      "title": "NNS FSD Test",
      "topics": [
        "NNS.FSD"
      ]
    },
    {
      "page": "NNS.FSD.uni",
      "title": "NNS FSD Test uni-directional",
      "topics": [
        "NNS.FSD.uni"
      ]
    },
    {
      "page": "NNS.gravity",
      "title": "NNS gravity",
      "topics": [
        "NNS.gravity"
      ]
    },
    {
      "page": "NNS.MC",
      "title": "NNS Monte Carlo Sampling",
      "topics": [
        "NNS.MC"
      ]
    },
    {
      "page": "NNS.meboot",
      "title": "NNS meboot",
      "topics": [
        "NNS.meboot"
      ]
    },
    {
      "page": "NNS.mode",
      "title": "NNS mode",
      "topics": [
        "NNS.mode"
      ]
    },
    {
      "page": "NNS.moments",
      "title": "NNS moments",
      "topics": [
        "NNS.moments"
      ]
    },
    {
      "page": "NNS.norm",
      "title": "NNS Normalization",
      "topics": [
        "NNS.norm"
      ]
    },
    {
      "page": "NNS.part",
      "title": "NNS Partition Map",
      "topics": [
        "NNS.part"
      ]
    },
    {
      "page": "NNS.reg",
      "title": "NNS Regression",
      "topics": [
        "NNS.reg"
      ]
    },
    {
      "page": "NNS.rescale",
      "title": "NNS rescale",
      "topics": [
        "NNS.rescale"
      ]
    },
    {
      "page": "NNS.SD.cluster",
      "title": "NNS SD-based Clustering",
      "topics": [
        "NNS.SD.cluster"
      ]
    },
    {
      "page": "NNS.SD.efficient.set",
      "title": "NNS SD Efficient Set",
      "topics": [
        "NNS.SD.efficient.set"
      ]
    },
    {
      "page": "NNS.seas",
      "title": "NNS Seasonality Test",
      "topics": [
        "NNS.seas"
      ]
    },
    {
      "page": "NNS.SS",
      "title": "NNS Stochastic Superiority",
      "topics": [
        "NNS.SS"
      ]
    },
    {
      "page": "NNS.SSD",
      "title": "NNS SSD Test",
      "topics": [
        "NNS.SSD"
      ]
    },
    {
      "page": "NNS.SSD.uni",
      "title": "NNS SSD Test uni-directional",
      "topics": [
        "NNS.SSD.uni"
      ]
    },
    {
      "page": "NNS.stack",
      "title": "NNS Stack",
      "topics": [
        "NNS.stack"
      ]
    },
    {
      "page": "NNS.TSD",
      "title": "NNS TSD Test",
      "topics": [
        "NNS.TSD"
      ]
    },
    {
      "page": "NNS.TSD.uni",
      "title": "NNS TSD Test uni-directional",
      "topics": [
        "NNS.TSD.uni"
      ]
    },
    {
      "page": "NNS.VAR",
      "title": "NNS VAR",
      "topics": [
        "NNS.VAR"
      ]
    },
    {
      "page": "PM.matrix",
      "title": "Partial Moment Matrix",
      "topics": [
        "PM.matrix"
      ]
    },
    {
      "page": "UPM",
      "title": "Upper Partial Moment",
      "topics": [
        "UPM"
      ]
    },
    {
      "page": "UPM.ratio",
      "title": "Upper Partial Moment Ratio",
      "topics": [
        "UPM.ratio"
      ]
    },
    {
      "page": "UPM.VaR",
      "title": "UPM VaR",
      "topics": [
        "UPM.VaR"
      ]
    }
  ],
  "_readme": "https://github.com/ovvo-financial/nns/raw/HEAD/README.md",
  "_rundeps": [
    "Rcpp",
    "RcppParallel"
  ],
  "_sysdeps": [
    {
      "shlib": "libstdc++",
      "package": "libstdc++6",
      "source": "gcc",
      "version": "16-20260322-1ubuntu1",
      "name": "c++",
      "homepage": "http://gcc.gnu.org/",
      "description": "GNU Standard C++ Library v3"
    }
  ],
  "_vignettes": [
    {
      "source": "NNSvignette_01_Overview.Rmd",
      "filename": "NNSvignette_01_Overview.html",
      "title": "Getting Started with NNS: Overview",
      "author": "Fred Viole",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Orientation",
        "1. Foundations — Partial Moments & Variance Decomposition",
        "1.1 Why partial moments",
        "1.2 Core functions and headers",
        "1.3 Code: variance decomposition & CDF",
        "2. Descriptive & Distributional Tools",
        "2.1 Higher moments from partial moments",
        "2.2 Mode estimation (no bin‑or‑bandwidth angst)",
        "2.3 CDF tables via LPM ratios",
        "3. Dependence & Nonlinear Association",
        "3.1 Why move beyond Pearson (r)",
        "3.2 Code: nonlinear dependence",
        "3.3 Code: copula",
        "4. Normalization and Rescaling",
        "4.1 Normalization",
        "4.2 Risk‑neutral rescale (pricing context)",
        "5. Hypothesis Testing, ANOVA & Stochastic Superiority",
        "5.1 Concept",
        "5.2 Code: two‑sample & multi‑group",
        "5.3 Stochastic Superiority",
        "6. Regression, Boosting, Stacking & Causality",
        "6.1 Philosophy",
        "6.2 Code: classification via regression + ensembles",
        "6.3 Code: directional causality",
        "7. Time Series & Forecasting",
        "8. Simulation & Bootstrap & Risk‑Neutral Rescaling",
        "8.1 Maximum entropy bootstrap (shape‑preserving)",
        "8.2 Monte Carlo over the full correlation space",
        "9. Portfolio & Stochastic Dominance",
        "Appendix A — Measure‑theoretic sketch (why partial moments are rigorous)",
        "Appendix B — Quick Reference (Grouped by Topic)",
        "Overall Theory",
        "1. Partial Moments & Ratios",
        "2. Descriptive Statistics & Distributions",
        "3. Dependence & Association",
        "4. Normalization & Rescaling",
        "5. Hypothesis Testing",
        "6. Regression, Classification & Causality",
        "7. Differentiation & Slope Measures",
        "8. Time Series & Forecasting",
        "9. Simulation & Bootstrap",
        "10. Portfolio Analysis & Stochastic Dominance"
      ],
      "created": "2026-06-04 16:35:27",
      "modified": "2026-06-29 19:13:47",
      "commits": 3
    },
    {
      "source": "NNSvignette_02_Partial_Moments.Rmd",
      "filename": "NNSvignette_02_Partial_Moments.html",
      "title": "Getting Started with NNS: Partial Moments",
      "author": "Fred Viole",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Partial Moments",
        "Mean",
        "Variance",
        "Standard Deviation",
        "First 4 Moments",
        "Statistical Mode of a Continuous Distribution",
        "Covariance",
        "Covariance Elements and Covariance Matrix",
        "Pearson Correlation",
        "CDFs (Discrete and Continuous)",
        "Numerical Integration",
        "Bayes' Theorem",
        "References"
      ],
      "created": "2026-06-04 16:35:27",
      "modified": "2026-06-29 19:13:47",
      "commits": 2
    },
    {
      "source": "NNSvignette_03_Correlation_and_Dependence.Rmd",
      "filename": "NNSvignette_03_Correlation_and_Dependence.html",
      "title": "Getting Started with NNS: Correlation and Dependence",
      "author": "Fred Viole",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Correlation and Dependence",
        "Linear Equivalence",
        "Nonlinear Relationship",
        "Cyclic Relationship",
        "Asymmetrical Analysis",
        "Dependence",
        "p-values for NNS.dep()",
        "Multivariate Dependence NNS.copula()",
        "References"
      ],
      "created": "2026-06-04 16:35:27",
      "modified": "2026-06-29 19:13:47",
      "commits": 2
    },
    {
      "source": "NNSvignette_04_Normalization_and_Rescaling.Rmd",
      "filename": "NNSvignette_04_Normalization_and_Rescaling.html",
      "title": "Getting Started with NNS: Normalization and Rescaling",
      "author": "Fred Viole",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "NNS.norm(): Normalize Multiple Variables",
        "Mathematical Structure",
        "Step 1: Compute Mean Vector",
        "Step 2: Construct Mean Ratio Matrix",
        "Step 3: Dependence Weight Matrix",
        "Step 4: Scaling Factors",
        "Linear Case Proof",
        "Nonlinear Case Interpretation",
        "[\\text{mean}(X_{\\cdot j}^{*})",
        "Examples",
        "Basic Multivariate Example",
        "Normalize list of unequal vector lengths",
        "Quantile Normalization Comparison",
        "Practical Applications",
        "NNS.rescale(): Distribution Rescaling",
        "1) Min-Max Scaling",
        "[x^",
        "Example",
        "2) Risk-Neutral Scaling",
        "Terminal Type",
        "Discounted Type",
        "Risk-Neutral Example",
        "Discounted Example",
        "Conceptual Summary",
        "NNS.norm()",
        "NNS.rescale()",
        "References"
      ],
      "created": "2026-06-04 16:35:27",
      "modified": "2026-06-29 19:13:47",
      "commits": 2
    },
    {
      "source": "NNSvignette_05_Sampling.Rmd",
      "filename": "NNSvignette_05_Sampling.html",
      "title": "Getting Started with NNS: Sampling and Simulation",
      "author": "Fred Viole",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Sampling",
        "CDFs",
        "Empirical CDF",
        "Lower Partial Moment CDF (LPM.ratio)",
        "LPM.ratio degree > 0",
        "Generating PDFs with (LPM.VaR)",
        "Simulation",
        "Bootstrapping (NNS.meboot)",
        "target_drift Specification",
        "Simulating a Multivariate Dependence Structure",
        "Compare Multivariate Dependence Structures",
        "Alternative Using NNS.meboot",
        "References"
      ],
      "created": "2026-06-04 16:35:27",
      "modified": "2026-06-30 05:53:47",
      "commits": 3
    },
    {
      "source": "NNSvignette_06_Comparing_Distributions.Rmd",
      "filename": "NNSvignette_06_Comparing_Distributions.html",
      "title": "Getting Started with NNS: Comparing Distributions",
      "author": "Fred Viole",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Comparing Distributions",
        "Test if Same Population",
        "Test if means are Equal",
        "Test if means are Unequal",
        "Medians",
        "Stochastic Superiority",
        "Stochastic Dominance",
        "Stochastic Dominant Efficient Sets",
        "Stochastic Dominant Clusters",
        "References"
      ],
      "created": "2026-06-04 16:35:27",
      "modified": "2026-06-29 19:13:47",
      "commits": 2
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    {
      "source": "NNSvignette_07_Clustering_and_Regression.Rmd",
      "filename": "NNSvignette_07_Clustering_and_Regression.html",
      "title": "Getting Started with NNS: Clustering and Regression",
      "author": "Fred Viole",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Clustering and Regression",
        "NNS Partitioning NNS.part()",
        "X-only Partitioning",
        "Clusters Used in Regression",
        "NNS Regression NNS.reg()",
        "Univariate:",
        "Multivariate:",
        "Inter/Extrapolation",
        "NNS Dimension Reduction Regression",
        "Threshold",
        "Classification",
        "Cross-Validation NNS.stack()",
        "Increasing Dimensions",
        "Smoothing Option",
        "Imputation",
        "Univariate Imputation",
        "Multivariate Imputation",
        "A Note on Uncertainty Propagation",
        "References"
      ],
      "created": "2026-06-04 16:35:27",
      "modified": "2026-06-29 19:13:47",
      "commits": 2
    },
    {
      "source": "NNSvignette_08_Classification.Rmd",
      "filename": "NNSvignette_08_Classification.html",
      "title": "Getting Started with NNS: Classification",
      "author": "Fred Viole",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Classification",
        "Splits vs. Partitions",
        "NNS Partitions",
        "NNS.boost()",
        "Cross-Validation Classification Using NNS.stack()",
        "Brief Notes on Other Parameters",
        "References"
      ],
      "created": "2026-06-04 16:35:27",
      "modified": "2026-06-29 19:13:47",
      "commits": 2
    },
    {
      "source": "NNSvignette_09_Forecasting.Rmd",
      "filename": "NNSvignette_09_Forecasting.html",
      "title": "Getting Started with NNS: Forecasting",
      "author": "Fred Viole",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Forecasting",
        "Linear Regression",
        "Nonlinear Regression",
        "Cross-Validation",
        "Cross-Validating All Combinations of seasonal.factor",
        "Extension of Estimates",
        "Brief Notes on Other Parameters",
        "Multivariate Time Series Forecasting",
        "References"
      ],
      "created": "2026-06-04 16:35:27",
      "modified": "2026-06-29 19:13:47",
      "commits": 2
    }
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  "_indexed": true,
  "_nocasepkg": "nns",
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