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 13.2},
    url = {https://CRAN.R-project.org/package=NNS},
  }

Thank you for your interest in NNS!