Other packages > Find by keyword >

SVMD  

Spearman Variational Mode Decomposition
View on CRAN: Click here


Download and install SVMD package within the R console
Install from CRAN:
install.packages("SVMD")

Install from Github:
library("remotes")
install_github("cran/SVMD")

Install by package version:
library("remotes")
install_version("SVMD", "0.1.0")



Attach the package and use:
library("SVMD")
Maintained by
Dr. Himadri Shekhar Roy
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-09-16
Latest Update: 2024-09-16
Description:
In practice, it is difficult to determine the number of decomposition modes, K, for Variational Mode Decomposition (VMD). To overcome this issue, this study offers Spearman Variational Mode Decomposition (SVMD), a method that uses the Spearman correlation coefficient to calculate the ideal mode number. Unlike the Pearson correlation coefficient, which only returns a perfect value when X and Y are linearly connected, the Spearman correlation can be calculated without knowing the probability distributions of X and Y. The Spearman correlation coefficient, also called Spearman's rank correlation coefficient, is a subset of a wider correlation coefficient. As VMD decomposes a signal, the Spearman correlation coefficient between the reconstructed and original sequences rises as the mode number K increases. Once the signal has been fully decomposed, subsequent increases in K cause the correlation to gradually level off. When the correlation reaches a specific level, VMD is said to have adequately decomposed the signal. Numerous experiments revealed that a threshold of 0.997 produces the best denoising effect, so the threshold is set at 0.997. This package has been developed using concept of Yang et al. (2021)<doi:10.1016/j.aej.2021.01.055>.
How to cite:
Dr. Himadri Shekhar Roy (2024). SVMD: Spearman Variational Mode Decomposition. R package version 0.1.0, https://cran.r-project.org/web/packages/SVMD. Accessed 21 Nov. 2024.
Previous versions and publish date:
No previous versions
Other packages that cited SVMD R package
View SVMD citation profile
Other R packages that SVMD depends, imports, suggests or enhances
Complete documentation for SVMD
Functions, R codes and Examples using the SVMD R package
Full SVMD package functions and examples
Downloads during the last 30 days
Get rewarded with contribution points by helping add
Reviews / comments / questions /suggestions ↴↴↴

Today's Hot Picks in Authors and Packages

deductive  
Data Correction and Imputation Using Deductive Methods
Attempt to repair inconsistencies and missing values in data records by using information from vali ...
Download / Learn more Package Citations See dependency  
SCBiclust  
Identifies Mean, Variance, and Hierarchically Clustered Biclusters
Identifies a bicluster, a submatrix of the data such that the features and observations within the s ...
Download / Learn more Package Citations See dependency  
kgschart  
KGS Rank Graph Parser
Restore underlining numeric data from rating history graph of KGS (an online platform of the game o ...
Download / Learn more Package Citations See dependency  
pkgdepends  
Package Dependency Resolution and Downloads
Find recursive dependencies of 'R' packages from various sources. Solve the dependencies to obtain ...
Download / Learn more Package Citations See dependency  
r2resize  
In-Text Resize for Images, Tables and Fancy Resize Containers in 'shiny', 'rmarkdown' and 'quarto' Documents
Automatic resizing toolbar for containers, images and tables. Various resizable or expandable contai ...
Download / Learn more Package Citations See dependency  
RcppHNSW  
'Rcpp' Bindings for 'hnswlib', a Library for Approximate Nearest Neighbors
'Hnswlib' is a C++ library for Approximate Nearest Neighbors. This package provides a minimal R int ...
Download / Learn more Package Citations See dependency  

23,229

R Packages

199,929

Dependencies

62,984

Author Associations

23,230

Publication Badges

© Copyright 2022 - present. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA