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/>).
| Version: |
12.0 |
| Depends: |
R (≥ 3.6.0) |
| Imports: |
data.table, doParallel, foreach, quantmod, Rcpp, RcppParallel, Rfast, rgl, xts, zoo |
| LinkingTo: |
Rcpp, RcppParallel |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2026-04-10 |
| DOI: |
10.32614/CRAN.package.NNS |
| Author: |
Fred Viole [aut, cre],
Roberto Spadim [ctb] |
| Maintainer: |
Fred Viole <ovvo.open.source at gmail.com> |
| BugReports: |
https://github.com/OVVO-Financial/NNS/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/OVVO-Financial/NNS |
| NeedsCompilation: |
yes |
| SystemRequirements: |
GNU make |
| Materials: |
README |
| In views: |
Econometrics |
| CRAN checks: |
NNS results |
Documentation:
| Reference manual: |
NNS.html , NNS.pdf
|
| Vignettes: |
Getting Started with NNS: 08. Classification (source, R code)
Getting Started with NNS: 07. Clustering and Regression (source, R code)
Getting Started with NNS: 06. Comparing Distributions (source, R code)
Getting Started with NNS: 03. Correlation and Dependence (source, R code)
Getting Started with NNS: 09. Forecasting (source, R code)
Getting Started with NNS: 04. Normalization and Rescaling (source, R code)
Getting Started with NNS: 01. Overview (source, R code)
Getting Started with NNS: 02. Partial Moments (source, R code)
Getting Started with NNS: 05. Sampling and Simulation (source, R code)
|
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