Package: NNS 11.3

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

Authors:Fred Viole [aut, cre], Roberto Spadim [ctb]

NNS_11.3.tar.gz
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NNS.pdf |NNS.html✨
NNS/json (API)

# Install 'NNS' in R:
install.packages('NNS', repos = c('https://ovvo-financial.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ovvo-financial/nns/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

clusteringeconometricsmachine-learningnonlinearnonparametricpartial-momentsstatisticstime-seriescpp

10.77 score 72 stars 3 packages 66 scripts 1.8k downloads 11 mentions 45 exports 45 dependencies

Last updated 8 hours agofrom:f79d632840. Checks:1 OK, 11 WARNING. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 28 2025
R-4.5-win-x86_64WARNINGMar 28 2025
R-4.5-mac-x86_64WARNINGMar 28 2025
R-4.5-mac-aarch64WARNINGMar 28 2025
R-4.5-linux-x86_64WARNINGMar 28 2025
R-4.4-win-x86_64WARNINGMar 28 2025
R-4.4-mac-x86_64WARNINGMar 28 2025
R-4.4-mac-aarch64WARNINGMar 28 2025
R-4.4-linux-x86_64WARNINGMar 28 2025
R-4.3-win-x86_64WARNINGMar 28 2025
R-4.3-mac-x86_64WARNINGMar 28 2025
R-4.3-mac-aarch64WARNINGMar 28 2025

Exports:Co.LPMCo.UPMD.LPMD.UPMdy.d_dy.dxLPMLPM.ratioLPM.VaRNNS.ANOVANNS.ARMANNS.ARMA.optimNNS.boostNNS.causNNS.CDFNNS.copulaNNS.depNNS.diffNNS.distanceNNS.FSDNNS.FSD.uniNNS.gravityNNS.MCNNS.mebootNNS.modeNNS.momentsNNS.normNNS.nowcastNNS.partNNS.regNNS.rescaleNNS.SD.clusterNNS.SD.efficient.setNNS.seasNNS.SSDNNS.SSD.uniNNS.stackNNS.term.matrixNNS.TSDNNS.TSD.uniNNS.VARPM.matrixUPMUPM.ratioUPM.VaR

Dependencies:base64encbslibcachemclicodetoolscurldata.tabledigestdoParallelevaluatefastmapfontawesomeforeachfsgluehighrhtmltoolshtmlwidgetsiteratorsjquerylibjsonliteknitrlatticelifecyclemagrittrmemoisemimequantmodR6rappdirsRcppRcppArmadilloRcppParallelRfastrglrlangrmarkdownsasstinytexTTRxfunxtsyamlziggzoo

Getting Started with NNS: Classification

Fred Viole

Rendered fromNNSvignette_Classification.Rmdusingknitr::rmarkdownon Mar 28 2025.

Last update: 2024-09-03
Started: 2019-05-14

Getting Started with NNS: Clustering and Regression

Fred Viole

Rendered fromNNSvignette_Clustering_and_Regression.Rmdusingknitr::rmarkdownon Mar 28 2025.

Last update: 2024-09-12
Started: 2017-03-22

Getting Started with NNS: Comparing Distributions

Fred Viole

Rendered fromNNSvignette_Comparing_Distributions.Rmdusingknitr::rmarkdownon Mar 28 2025.

Last update: 2025-02-14
Started: 2023-07-05

Getting Started with NNS: Correlation and Dependence

Fred Viole

Rendered fromNNSvignette_Correlation_and_Dependence.Rmdusingknitr::rmarkdownon Mar 28 2025.

Last update: 2024-10-14
Started: 2017-03-22

Getting Started with NNS: Forecasting

Fred Viole

Rendered fromNNSvignette_Forecasting.Rmdusingknitr::rmarkdownon Mar 28 2025.

Last update: 2025-01-03
Started: 2017-03-23

Getting Started with NNS: Partial Moments

Fred Viole

Rendered fromNNSvignette_Partial_Moments.Rmdusingknitr::rmarkdownon Mar 28 2025.

Last update: 2024-12-16
Started: 2017-03-22

Getting Started with NNS: Sampling and Simulation

Fred Viole

Rendered fromNNSvignette_Sampling.Rmdusingknitr::rmarkdownon Mar 28 2025.

Last update: 2024-12-02
Started: 2023-08-04