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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