NNS: Nonlinear Nonparametric Statistics (original) (raw)
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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