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NNS: Nonlinear Nonparametric Statistics
NNS (Nonlinear Nonparametric Statistics) leverages partial moments – the fundamental elements of variance that asymptotically approximate the area under 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, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization, Stochastic superiority / dominance and Advanced Monte Carlo sampling. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995, Second edition: <https://ovvo-financial.github.io/NNS/book/>).
Getting started
README.md
Getting Started with NNS: Classification'
Getting Started with NNS: Clustering and Regression"
Getting Started with NNS: Comparing Distributions"
Getting Started with NNS: Correlation and Dependence"
Getting Started with NNS: Forecasting"
Getting Started with NNS: Normalization and Rescaling"
Getting Started with NNS: Overview"
Getting Started with NNS: Partial Moments"
Getting Started with NNS: Sampling and Simulation"
Browse package contents
Package details |
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|---|---|
| Author | Fred Viole [aut, cre], Roberto Spadim [ctb] |
| Maintainer | Fred Viole <ovvo.open.source@gmail.com> |
| License | GPL-3 |
| Version | 12.0 |
| URL | https://github.com/OVVO-Financial/NNS |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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