NNS
13.2Nonlinear Nonparametric Statistics
Overview
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/).
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- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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- ERROR2026-04-1810 OK · 3 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
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- NOTE2026-03-1011 OK · 3 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 31%
- Documented parameters
- 98%
- Return-value docs
- 100%
- References docs
- 78%
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Dependencies
Code & Tests
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122 releases. Pick two to compare their code metrics. R releases are shown for context.
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Package metadata
- First published
- 2016-04-05
- Total releases
- 122 / 10 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.6.0
- Download size
- 2.0 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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