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).
| Version: | 11.6.2 | 
| 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: | 2025-10-04 | 
| DOI: | 10.32614/CRAN.package.NNS | 
| Author: | Fred Viole [aut, cre],
  Roberto Spadim [ctb] | 
| Maintainer: | Fred Viole  <ovvo.financial.systems at gmail.com> | 
| BugReports: | https://github.com/OVVO-Financial/NNS/issues | 
| License: | GPL-3 | 
| NeedsCompilation: | yes | 
| SystemRequirements: | GNU make | 
| Materials: | README | 
| In views: | Econometrics | 
| CRAN checks: | NNS results | 
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