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NNS

NNS (Nonlinear Nonparametric Statistics) leverages partial moments – the fundamental elements of variance that asymptotically approximate the area of f(x) – to provide a robust foundation for nonlinear analysis while maintaining linear equivalences. Designed for real-world data that violates symmetry, linearity, or distributional assumptions.

NNS delivers a comprehensive suite of advanced statistical techniques, including: - Numerical Integration & Numerical Differentiation - Partitional & Hierarchical Clustering - Nonlinear Correlation & Dependence - Causal Analysis - Nonlinear Regression & Classification - ANOVA - Seasonality & Autoregressive Modeling - Normalization - Stochastic Superiority / Dominance - Advanced Monte Carlo Sampling

Companion R-package and datasets to: #### Viole, F. and Nawrocki, D. (2013) “Nonlinear Nonparametric Statistics: Using Partial Moments” (ISBN: 1490523995)

2nd edition available here: https://ovvo-financial.github.io/NNS/book/

For a direct quantitative finance implementation of NNS, see OVVO Labs

Current Version

Current CRAN version is

Installation

requires . See https://cran.r-project.org/ or for upgrading to latest R release.

library(remotes); remotes::install_github('OVVO-Financial/NNS', ref = "NNS-Beta-Version")

or via CRAN

install.packages('NNS')

Examples

Please see https://github.com/OVVO-Financial/NNS/blob/NNS-Beta-Version/examples/index.md for basic partial moments equivalences, hands-on statistics, machine learning and econometrics examples.

Citation

@Manual{,
    title = {NNS: Nonlinear Nonparametric Statistics},
    author = {Fred Viole},
    year = {2016},
    note = {R package version 12.0},
    url = {https://CRAN.R-project.org/package=NNS},
  }

Thank you for your interest in NNS!

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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