Package: ivaBSS 1.0.0

Mika Sipilä

ivaBSS: Tools for Independent Vector Analysis

Independent vector analysis (IVA) is a blind source separation (BSS) model where several datasets are jointly unmixed. This package provides several methods for the unmixing together with some performance measures. For details, see Anderson et al. (2011) <doi:10.1109/TSP.2011.2181836> and Lee et al. (2007) <doi:10.1016/j.sigpro.2007.01.010>.

Authors:Mika Sipilä [aut, cre], Klaus Nordhausen [aut], Sara Taskinen [aut]

ivaBSS_1.0.0.tar.gz
ivaBSS_1.0.0.tar.gz(r-4.5-noble)ivaBSS_1.0.0.tar.gz(r-4.4-noble)
ivaBSS_1.0.0.tgz(r-4.4-emscripten)ivaBSS_1.0.0.tgz(r-4.3-emscripten)
ivaBSS.pdf |ivaBSS.html✨
ivaBSS/json (API)
NEWS

# Install 'ivaBSS' in R:
install.packages('ivaBSS', repos = 'https://cloud.r-project.org')

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 119 downloads 11 exports 3 dependencies

Last updated 3 years agofrom:3325b23f4f. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 15 2025
R-4.5-linuxOKMar 15 2025
R-4.4-linuxOKMar 15 2025

Exports:avg_ISIcoef.ivacomponents.ivafastIVAjbss_achievedjoint_ISINewtonIVAplot.ivapredict.ivaprint.ivasummary.iva

Dependencies:BSSprepRcppRcppArmadillo

Citation

To cite package ‘ivaBSS’ in publications use:

Sipilä M, Nordhausen K, Taskinen S (2022). ivaBSS: Tools for Independent Vector Analysis. R package version 1.0.0, https://CRAN.R-project.org/package=ivaBSS.

Corresponding BibTeX entry: 

  @Manual{,
    title = {ivaBSS: Tools for Independent Vector Analysis},
    author = {Mika Sipilä and Klaus Nordhausen and Sara Taskinen},
    year = {2022},
    note = {R package version 1.0.0},
    url = {https://CRAN.R-project.org/package=ivaBSS},
  }

Readme and manuals

R Package for Independent Vector Analysis

Independent vector analysis (IVA) is a blind source separation (BSS) model where several datasets are jointly unmixed. This package provides several methods for the unmixing together with some performance measures.

How to install the package?

Make sure you have git installed and clone the package using:

git clone https://github.com/mikasip/IVA.git

or just download ivaBSS_1.0.0.tar.gz file from this repository.

Make sure you have R in your environment variables, open command prompt and run:

R CMD INSTALL path_to_file/ivaBSS_1.0.0.tar.gz

How to use?

The package is used to estimate source vectors by unmixing the observed mixtures. The next example generates mixtures from sources following multivariate Laplace distribution and unmixes them using Newton update based IVA with multivariate Gaussian source density model.

if (require("LaplacesDemon")) {
  # Generate sources from multivariate Laplace distribution
  P <- 4; N <- 1000; D <- 4;
  S <- array(NA, c(P, N, D))
      
  for (i in 1:P) {
    U <- array(rnorm(D * D), c(D, D))
    Sigma <- crossprod(U)
    S[i, , ] <- rmvl(N, rep(0, D), Sigma)
  }
    
  # Generate mixing matrices from standard normal distribution
  A <- array(rnorm(P * P * D), c(P, P, D))
    
  # Generate mixtures
  X <- array(NaN, c(P, N, D))
  for (d in 1:D) {
    X[, , d] <- A[, , d] \%*\% S[, , d]
  }
    
  # Estimate sources and unmixing matrices
  res_G <- NewtonIVA(X, source_density = "gaussian")
  }
}