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NNS
Nonlinear Nonparametric Statistics
View on CRAN: Click
here
Download and install NNS package within the R console
Install from CRAN:
install.packages("NNS")
Install from Github:
library("remotes")
install_github("cran/NNS") Install by package version:
library("remotes")
install_version("NNS", "11.1") Attach the package and use:
library("NNS")
Maintained by
Fred Viole
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.NNS . https://github.com/OVVO-Financial/NNS/issues . NNS results . NNS.pdf . Getting Started with NNS: Classification . Getting Started with NNS: Clustering and Regression . Getting Started with NNS: Comparing Distributions . Getting Started with NNS: Correlation and Dependence . Getting Started with NNS: Forecasting . Getting Started with NNS: Partial Moments . Getting Started with NNS: Sampling and Simulation . NNS_11.2.tar.gz . NNS_11.2.zip . NNS_11.2.zip . NNS_11.2.zip . NNS_11.2.tgz . NNS_11.2.tgz . NNS_11.2.tgz . NNS_11.2.tgz . NNS_11.2.tgz . NNS_11.2.tgz . NNS archive . https://CRAN.R-project.org/package=NNS .
First Published: 2016-04-05
Latest Update: 2024-03-07
Description:
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).
How to cite:
Fred Viole (2016). NNS: Nonlinear Nonparametric Statistics. R package version 11.1, https://cran.r-project.org/web/packages/NNS. Accessed 13 Apr. 2025.
Previous versions and publish date:
0.1.1 (2016-04-06 15:56), 0.1.3 (2016-04-21 22:12), 0.1.6 (2016-05-11 06:18), 0.1.9.2 (2016-06-14 20:34), 0.1.9.4 (2016-07-30 09:30), 0.1.9 (2016-05-29 16:48), 0.1 (2016-04-05 08:14), 0.2.0 (2016-08-18 10:02), 0.2.1 (2016-09-13 21:19), 0.2.2 (2016-09-29 08:02), 0.2.3 (2016-10-21 18:41), 0.2.4 (2016-11-28 08:21), 0.2.5 (2017-01-11 00:39), 0.2.6 (2017-02-12 08:31), 0.3.0.1 (2017-03-11 18:56), 0.3.0 (2017-03-10 00:43), 0.3.1 (2017-03-31 18:24), 0.3.2 (2017-05-01 12:19), 0.3.3 (2017-06-03 00:22), 0.3.4 (2017-06-27 17:50), 0.3.5 (2017-07-23 18:24), 0.3.6 (2017-08-15 23:39), 0.3.7 (2017-09-30 20:47), 0.3.8.1 (2017-12-08 17:07), 0.3.8.2 (2018-01-08 20:57), 0.3.8.3 (2018-02-16 20:06), 0.3.8.4 (2018-03-17 07:21), 0.3.8.6 (2018-04-16 13:11), 0.3.8.7 (2018-05-15 18:46), 0.3.8.8 (2019-03-04 14:20), 0.3.8 (2017-11-13 06:29), 0.3.9 (2019-04-15 16:22), 0.4.0 (2019-05-14 20:30), 0.4.1 (2019-06-10 23:40), 0.4.2 (2019-06-11 18:20), 0.4.3 (2019-07-19 18:40), 0.4.4 (2019-08-08 17:50), 0.4.5 (2019-09-09 18:40), 0.4.6 (2019-10-07 17:40), 0.4.7.1 (2019-11-21 01:00), 0.4.7 (2019-11-19 19:20), 0.4.8 (2020-01-08 11:00), 0.4.9 (2020-02-13 18:00), 0.5.0 (2020-03-17 15:40), 0.5.1 (2020-04-15 20:20), 0.5.2.1 (2020-05-19 16:00), 0.5.2 (2020-05-17 17:50), 0.5.3 (2020-06-20 00:50), 0.5.4.1 (2020-07-01 14:00), 0.5.4.2 (2020-07-01 15:10), 0.5.4.3 (2020-08-01 02:20), 0.5.4 (2020-06-29 18:30), 0.5.5 (2020-09-02 08:50), 0.5.6 (2020-12-03 19:50), 0.5.7 (2021-01-05 15:20), 0.6.1 (2021-03-15 02:50), 0.6.2 (2021-03-15 13:50), 0.6.3.1 (2021-04-20 23:30), 0.6.3.2 (2021-04-21 11:30), 0.6 (2021-02-04 06:50), 0.7.0.1 (2021-05-27 02:20), 0.7.0 (2021-05-25 18:00), 0.7.1 (2021-06-26 01:20), 0.7.2 (2021-08-06 23:40), 0.8.0 (2021-09-13 17:30), 0.8.1 (2021-09-20 20:30), 0.8.2 (2021-09-27 16:00), 0.8.3 (2021-11-23 17:30), 0.8.4.1 (2022-01-13 11:12), 0.8.4 (2022-01-12 14:52), 0.8.5 (2022-03-08 17:30), 0.8.61 (2022-04-05 00:50), 0.8.70 (2022-04-24 16:30), 0.9.0 (2022-08-06 09:30), 0.9.1 (2022-08-22 09:50), 0.9.2.1 (2022-09-29 13:10), 0.9.2 (2022-09-23 22:00), 0.9.3 (2022-11-03 23:20), 0.9.4 (2022-12-01 20:30), 0.9.5 (2023-01-08 01:20), 0.9.6.1 (2023-03-08 17:40), 0.9.6 (2023-03-08 09:50), 0.9.7 (2023-04-11 22:50), 0.9.8 (2023-05-17 08:30), 0.9.9.1 (2023-06-15 06:30), 0.9.9 (2023-05-19 11:50), 10.0 (2023-07-15 23:30), 10.1 (2023-08-26 15:50), 10.2 (2023-10-03 15:00), 10.3 (2023-11-10 11:00), 10.4 (2023-11-27 20:20), 10.5 (2024-01-10 11:23), 10.6 (2024-02-20 09:10), 10.7 (2024-03-07 01:10), 10.8.1 (2024-05-11 19:33), 10.8.2 (2024-05-12 10:03), 10.8 (2024-04-19 09:02), 10.9.1 (2024-08-23 17:20), 10.9.2 (2024-09-06 16:30), 10.9.3 (2024-10-14 17:40), 10.9.4 (2024-12-02 19:10), 10.9.5 (2024-12-16 18:50), 10.9.6 (2024-12-17 18:40), 10.9 (2024-08-19 08:50), 11.0 (2025-01-10 19:30), 11.1 (2025-02-17 21:00)
Other packages that cited NNS R package
View NNS citation profile
Other R packages that NNS depends,
imports, suggests or enhances
Complete documentation for NNS
Functions, R codes and Examples using
the NNS R package
Some associated functions: Co.LPM . Co.UPM . D.LPM . D.UPM . LPM.VaR . LPM . LPM.ratio . NNS.ANOVA . NNS.ARMA . NNS.ARMA.optim . NNS.CDF . NNS.FSD . NNS.FSD.uni . NNS.PDF . NNS.SD.efficient.set . NNS.SSD . NNS.SSD.uni . NNS.TSD . NNS.TSD.uni . NNS.VAR . NNS.boost . NNS.caus . NNS.copula . NNS.dep . NNS.diff . NNS.distance . NNS.gravity . NNS.meboot . NNS.mode . NNS.moments . NNS.norm . NNS.nowcast . NNS.part . NNS.reg . NNS.seas . NNS.stack . NNS.term.matrix . NNS_bin . PM.matrix . UPM.VaR . UPM . UPM.ratio . dy.d_ . dy.dx .
Some associated R codes: ANOVA.R . ARMA.R . ARMA_optim.R . Binary_ANOVA.R . Boost.R . Causal_matrix.R . Causation.R . Central_tendencies.R . Copula.R . Dependence.R . Dependence_matrix.R . FSD.R . Internal_Functions.R . LPM_UPM_VaR.R . Multivariate_Regression.R . NNS_Distance.R . NNS_VAR.R . NNS_meboot.R . NNS_term_matrix.R . Normalization.R . Nowcast.R . Numerical_Differentiation.R . Partial_Moments.R . Partition_Map.R . RcppExports.R . Regression.R . SD_Efficient_Set.R . SSD.R . Seasonality_Test.R . Stack.R . TSD.R . Uni_Causation.R . Uni_SD_Routines.R . dy_d_wrt.R . dy_dx.R . gvload.R . Full NNS package functions and examples
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