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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. 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 dominance and Advanced Monte Carlo sampling. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).
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: 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.financial.systems@gmail.com> |
| License | GPL-3 |
| Version | 11.4.1 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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