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: |
11.4.1 |
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
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