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:	0.9.1
Depends:	R (≥ 3.5.0), doParallel
Imports:	caret, data.table, dtw, dynlm, meboot, MESS, Quandl, Rcpp, RcppParallel, Rfast, rgl, stringr, tseries, zoo
LinkingTo:	Rcpp, RcppParallel
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Published:	2022-08-22
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:

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

Downloads:

Package source:	NNS_0.9.1.tar.gz
Windows binaries:	r-devel: NNS_0.9.1.zip, r-release: NNS_0.9.1.zip, r-oldrel: NNS_0.9.1.zip
macOS binaries:	r-release (arm64): NNS_0.9.1.tgz, r-oldrel (arm64): NNS_0.9.1.tgz, r-release (x86_64): NNS_0.9.1.tgz, r-oldrel (x86_64): NNS_0.9.1.tgz
Old sources:	NNS archive

Reverse dependencies:

Reverse suggests:

influential

Linking:

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