NNS: Nonlinear Nonparametric Statistics

Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modelling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).

Version: 0.3.5
Depends: R (≥ 3.3.0), data.table, rgl, stringr
Suggests: knitr, rmarkdown
Published: 2017-07-23
Author: Fred Viole
Maintainer: Fred Viole <ovvo.financial.systems at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: NNS results


Reference manual: NNS.pdf
Vignettes: Getting Started with NNS: Clustering and Regression
Getting Started with NNS: Correlation and Dependence
Getting Started with NNS: Forecasting
Getting Started with NNS: Partial Moments
Package source: NNS_0.3.5.tar.gz
Windows binaries: r-devel: NNS_0.3.5.zip, r-release: NNS_0.3.5.zip, r-oldrel: NNS_0.3.5.zip
OS X El Capitan binaries: r-release: NNS_0.3.5.tgz
OS X Mavericks binaries: r-oldrel: NNS_0.3.5.tgz
Old sources: NNS archive


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