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
- Partitional & Hierarchial Clustering
- Nonlinear Correlation & Dependence
- Causal Analysis
- Nonlinear Regression & Classification
- ANOVA
- Seasonality & Autoregressive Modeling
- Normalization
- Stochastic Dominance
- Advanced Monte Carlo Sampling
Companion R-package and datasets to:
Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" (ISBN: 1490523995)
For a quantitative finance implementation of NNS, see OVVO Labs
Current CRAN version is
requires
. See https://cran.r-project.org/ or
for upgrading to latest R release.
library(remotes); remotes::install_github('OVVO-Financial/NNS', ref = "NNS-Beta-Version")or via CRAN
install.packages('NNS')Please see https://github.com/OVVO-Financial/NNS/blob/NNS-Beta-Version/examples/index.md for basic partial moments equivalences, hands-on statistics, machine learning and econometrics examples.
@Manual{,
title = {NNS: Nonlinear Nonparametric Statistics},
author = {Fred Viole},
year = {2016},
note = {R package version 11.6.2},
url = {https://CRAN.R-project.org/package=NNS},
}