Package: NNS 10.9.3

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_10.9.3.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'))

Peer review:

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

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

On CRAN:

clusteringeconometricsmachine-learningnonlinearnonparametricpartial-momentsstatisticstime-series

45 exports 62 stars 4.54 score 46 dependencies 2 dependents 11 mentions 67 scripts 2.1k downloads

Last updated 6 days agofrom:ed474a1cd0. Checks:OK: 1 WARNING: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 12 2024
R-4.5-win-x86_64WARNINGSep 12 2024
R-4.5-linux-x86_64WARNINGSep 12 2024
R-4.4-win-x86_64WARNINGSep 12 2024
R-4.4-mac-x86_64WARNINGSep 12 2024
R-4.4-mac-aarch64WARNINGSep 12 2024
R-4.3-win-x86_64WARNINGSep 12 2024
R-4.3-mac-x86_64WARNINGSep 12 2024
R-4.3-mac-aarch64WARNINGSep 12 2024

Exports:Co.LPMCo.UPMD.LPMD.UPMdy.d_dy.dxLPMLPM.ratioLPM.VaRNNS_binNNS.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.efficient.setNNS.seasNNS.SSDNNS.SSD.uniNNS.stackNNS.term.matrixNNS.TSDNNS.TSD.uniNNS.VARPM.matrixUPMUPM.ratioUPM.VaR

Dependencies:base64encbslibcachemclicodetoolscurldata.tabledigestdoParallelevaluatefastmapfontawesomeforeachfsgluehighrhtmltoolshtmlwidgetsiteratorsjquerylibjsonliteknitrlatticelifecyclemagrittrmemoisemimequantmodR6rappdirsRcppRcppArmadilloRcppGSLRcppParallelRcppZigguratRfastrglrlangrmarkdownsasstinytexTTRxfunxtsyamlzoo

Getting Started with NNS: Classification

Fred Viole

Rendered fromNNSvignette_Classification.Rmdusingknitr::rmarkdownon Sep 12 2024.

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 Sep 12 2024.

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

Getting Started with NNS: Comparing Distributions

Fred Viole

Rendered fromNNSvignette_Comparing_Distributions.Rmdusingknitr::rmarkdownon Sep 12 2024.

Last update: 2024-09-03
Started: 2023-07-05

Getting Started with NNS: Correlation and Dependence

Fred Viole

Rendered fromNNSvignette_Correlation_and_Dependence.Rmdusingknitr::rmarkdownon Sep 12 2024.

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

Getting Started with NNS: Forecasting

Fred Viole

Rendered fromNNSvignette_Forecasting.Rmdusingknitr::rmarkdownon Sep 12 2024.

Last update: 2024-09-05
Started: 2017-03-23

Getting Started with NNS: Partial Moments

Fred Viole

Rendered fromNNSvignette_Partial_Moments.Rmdusingknitr::rmarkdownon Sep 12 2024.

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

Getting Started with NNS: Sampling and Simulation

Fred Viole

Rendered fromNNSvignette_Sampling.Rmdusingknitr::rmarkdownon Sep 12 2024.

Last update: 2024-09-03
Started: 2023-08-04