NNS (Nonlinear Nonparametric Statistics) leverages partial moments â the fundamental elements of variance that asymptotically approximate the area under f(x) â 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: 11.4.1 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: 2025-07-15 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 Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=NNS to link to this page.
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