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 modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).

Version: 0.5.6
Depends: R (≥ 3.3.0), doParallel
Imports: caret, data.table, dtw, meboot, Rfast, rgl, stringr, tdigest
Suggests: knitr, rmarkdown
Published: 2020-12-03
Author: Fred Viole
Maintainer: Fred Viole <ovvo.financial.systems at gmail.com>
BugReports: https://github.com/OVVO-Financial/NNS/issues
License: GPL-3
NeedsCompilation: no
Materials: README
In views: Econometrics
CRAN checks: NNS results


Reference manual: NNS.pdf
Vignettes: Getting Started with NNS: Classification
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_0.5.6.tar.gz
Windows binaries: r-devel: NNS_0.5.6.zip, r-release: NNS_0.5.6.zip, r-oldrel: NNS_0.5.6.zip
macOS binaries: r-release: NNS_0.5.6.tgz, r-oldrel: NNS_0.5.6.tgz
Old sources: NNS archive

Reverse dependencies:

Reverse suggests: influential


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