[{"_1":2,"_176":-5,"_177":-5},"loaderData",{"_3":4,"_13":14,"_21":22,"_151":152},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","routes/_layout",{"_15":-5,"_16":17,"_18":17,"_19":11,"_20":11},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_23":24,"_25":-5,"_26":11,"_27":11,"_28":29,"_30":31,"_32":-5,"_33":11,"_34":35,"_86":-5,"_87":88,"_142":-5,"_143":11,"_144":17,"_145":11,"_146":-5,"_147":11,"_148":-5,"_149":150},"canonicalUrl","https://hub.docker.com/r/mlampros/vmdecomp/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","mlampros","reponame","vmdecomp","cloudBuildSource","isAutobuildsAvailable","repo",{"_36":37,"_38":39,"_42":43,"_44":45,"_46":11,"_47":48,"_49":50,"_51":52,"_55":31,"_28":29,"_56":57,"_58":41,"_59":60,"_61":62,"_63":64,"_65":-5,"_66":60,"_67":68,"_69":11,"_70":29,"_71":72,"_77":11,"_78":79,"_83":29,"_84":85},"categories",[],"content_types",[40,41],"buildkit_cache","image","date_registered","2022-06-08T14:02:28.727568Z","description","R package for 'Variational Mode Decomposition' (1- and 2-dimensional)","is_private","last_updated","2025-09-30T17:14:23.018608Z","last_modified","2025-09-30T17:14:23.459909Z","media_types",[53,54],"application/vnd.buildkit.cacheconfig.v0","application/vnd.oci.image.index.v1+json","name","pull_count",220,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","\n[![R-CMD-check](https://github.com/mlampros/VMDecomp/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/mlampros/VMDecomp/actions/workflows/R-CMD-check.yaml)\n[![docs: passing](https://img.shields.io/badge/docs-passing-success.svg)](https://mlampros.github.io/VMDecomp/reference/index.html)\n[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/VMDecomp)](http://cran.r-project.org/package=VMDecomp)\n[![Downloads](http://cranlogs.r-pkg.org/badges/grand-total/VMDecomp?color=blue)](http://www.r-pkg.org/pkg/VMDecomp)\n[![](https://img.shields.io/docker/automated/mlampros/vmdecomp.svg)](https://hub.docker.com/r/mlampros/vmdecomp)\n[![status](https://tinyverse.netlify.app/badge/VMDecomp)](https://CRAN.R-project.org/package=VMDecomp)\n\n\n## VMDecomp\n\n
\n\nThe **VMDecomp** R package is the *RcppArmadillo* implementation of the [\"Variational Mode Decomposition\" Matlab code](https://math.montana.edu/dzosso/code/) in R. More details on the functionality of VMDecomp can be found in the package [Documentation](https://mlampros.github.io/VMDecomp/reference/index.html), [Vignette](https://mlampros.github.io/VMDecomp/articles/variatonal_mode_decomposition.html) and [blog post](http://mlampros.github.io/2022/06/11/variatonal_mode_decomposition/).\n\n
\n\n### Examples:\n\n
\n\n**Variational Mode Decomposition** (including residuals)\n\n
\n\n```R\n\nrequire(VMDecomp)\ndata(arrhythmia)\n\nalpha = 2000 # moderate bandwidth constraint\ntau = 0 # noise-tolerance (no strict fidelity enforcement)\nK = 9 # 9 modes\nDC = FALSE # no DC part imposed\ninit = 1 # initialize omegas uniformly\ntol = 1e-6\n\nvec_arrhythmia = arrhythmia[['MLII']]\n\nset.seed(1)\narr_vmd = vmd(data = vec_arrhythmia, \n alpha = alpha,\n tau = tau, \n K = K, \n DC = DC,\n init = init, \n tol = tol,\n verbose = TRUE)\n \nimfs = data.table::data.table(arr_vmd$u)\ncolnames(imfs) = glue::glue(\"IMF_{1:ncol(imfs)}\")\nimfs$residual = rowSums(imfs) - vec_arrhythmia\n\nround(imfs, digits = 5)\n\n# IMF_1 IMF_2 IMF_3 IMF_4 IMF_5 IMF_6 IMF_7 IMF_8 IMF_9 residual\n# 1: -0.06947 0.07831 0.13355 0.14031 0.10371 0.05622 -0.00143 -0.09686 -0.00629 -0.01194\n# 2: -0.06971 0.07765 0.13199 0.13698 0.09920 0.05226 -0.00062 -0.07683 -0.01055 -0.00963\n# 3: -0.07016 0.07639 0.12896 0.13047 0.09046 0.04475 0.00097 -0.04075 -0.01487 -0.00377\n# 4: -0.07068 0.07468 0.12466 0.12114 0.07810 0.03447 0.00329 0.00427 -0.01331 0.00662\n# 5: -0.07108 0.07273 0.11937 0.10947 0.06297 0.02246 0.00622 0.04929 -0.00211 0.01932\n# --- \n# 9996: -0.07001 -0.13154 -0.24738 0.18826 0.03381 -0.07354 0.00076 0.03773 0.01426 0.01234\n# 9997: -0.06980 -0.13333 -0.25498 0.21256 0.03400 -0.11214 0.05833 -0.00481 0.01450 0.00432\n# 9998: -0.06951 -0.13452 -0.26056 0.23154 0.03414 -0.14316 0.10925 -0.04580 0.00791 -0.00570\n# 9999: -0.06934 -0.13534 -0.26432 0.24447 0.03413 -0.16496 0.14706 -0.07813 -0.00137 -0.00780\n# 10000: -0.06932 -0.13581 -0.26626 0.25095 0.03402 -0.17629 0.16708 -0.09601 -0.00797 0.00040\n\n```\n\n
\n\n**Estimation of the K-modes Parameter** (*correlation threshold* of 0.1 and a *minimum K* of 2)\n\n
\n\n```R\n\nrequire(VMDecomp)\ndata(arrhythmia)\n\ndefault_vmd_params = list(alpha = 2000,\n tau = 0,\n DC = FALSE,\n init = 1,\n tol = 1e-6)\n\nres_k = estimate_k_modes(signal_1d = arrhythmia[['MLII']],\n cor_thresh = 0.1,\n default_vmd_params = default_vmd_params,\n min_K = 2,\n seed = 1,\n verbose = TRUE)\n \n# VMD based on a K of '2' will be computed ... \n# VMD based on a K of '3' will be computed ... \n# VMD based on a K of '4' will be computed ... \n# VMD based on a K of '5' will be computed ... \n# VMD based on a K of '6' will be computed ... \n# VMD based on a K of '7' will be computed ... \n# VMD based on a K of '8' will be computed ... \n# VMD based on a K of '9' will be computed ... \n# Optimal K parameter: '8' Pre-specified correlation coefficient threshold: '0.1'\n# Elapsed time: 0 hours and 1 minutes and 19 seconds.\n\nres_k\n# [1] 8\n\n```\n\n
\n\n### Installation:\n\n
\n\nTo install the package from CRAN use, \n\n```R\ninstall.packages(\"VMDecomp\")\n\n```\n
\n\nor download the latest version from Github using the *pak* package,\n\n
\n\n```R\npak::pak('mlampros/VMDecomp')\n\n```\n\n
\n\n#### **Docker Image**\n\n
\n\n**Docker images** of the *VMDecomp* package are available to download from my [dockerhub](https://hub.docker.com/r/mlampros/vmdecomp) account. The images come with *Rstudio* and the *R-development* version (latest) installed. The whole process was tested on Ubuntu 18.04. To **pull** & **run** the image do the following,\n\n
\n\n```R\n\ndocker pull mlampros/vmdecomp:rstudiodev\n\ndocker run -d --name rstudio_dev -e USER=rstudio -e PASSWORD=give_here_your_password --rm -p 8787:8787 mlampros/vmdecomp:rstudiodev\n\n```\n\n
\n\nThe user can also **bind** a home directory / folder to the image to use its files by specifying the **-v** command,\n\n
\n\n```R\n\ndocker run -d --name rstudio_dev -e USER=rstudio -e PASSWORD=give_here_your_password --rm -p 8787:8787 -v /home/YOUR_DIR:/home/rstudio/YOUR_DIR mlampros/vmdecomp:rstudiodev\n\n\n```\n\n
\n\nThe **USER** defaults to *rstudio* but you have to give your **PASSWORD** of preference (see [rocker-project.org](https://rocker-project.org/) for more information).\n\n
\n\nOpen your web-browser and depending where the docker image was *build / run* give, \n\n
\n\n**1st. Option** on your personal computer,\n\n
\n\n```R\nhttp://0.0.0.0:8787 \n\n```\n\n
\n\n**2nd. Option** on a cloud instance, \n\n
\n\n```R\nhttp://Public DNS:8787\n\n```\n\n
\n\nto access the Rstudio console in order to give your username and password.\n\n
\n\n### **Similar Projects:**\n\n* https://github.com/vrcarva/vmdpy (*Variational Mode Decomposition in Python*)\n* https://github.com/helske/Rlibeemd (*ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN)*) \n\n
\n\n### **Citation:**\n\nIf you use the **VMDecomp** R package in your paper or research please cite both **VMDecomp** and the **original articles / software** `https://CRAN.R-project.org/package=VMDecomp`:\n\n
\n\n```R\n@Manual{,\n title = {{VMDecomp}: Variational Mode Decomposition using R},\n author = {Lampros Mouselimis},\n year = {2025},\n note = {R package version 1.0.2},\n url = {https://CRAN.R-project.org/package=VMDecomp},\n}\n```\n\n
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mlampros/vmdecomp

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