Nonlinear Nonparametric Statistics R package NNS version 12.1 CRAN Task View: Econometrics (https://CRAN.R-project.org/view=Econometrics) 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/>). Software Depends: R (>= 3.6.0) Imports: data.table, doParallel, foreach, Rcpp, RcppParallel, Rfast, rgl, xts, zoo LinkingTo: Rcpp, RcppParallel Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Fred Viole <ovvo.open.source@gmail.com> Comprehensive R Archive Network (CRAN) Fred Viole [aut, cre], Roberto Spadim [ctb] GPL-3 2026-06-05 application/tgz https://CRAN.R-project.org/package=NNS doi:10.32614/CRAN.package.NNS