neurobase, oro.nifti, gridExtra, ggplot2, gtools

Authors@R: person("Phillip", "Murray", email = "phillip.murray18@imperial.ac.uk",
Description: Efficient simulation of Brownian semistationary (BSS) processes using the hybrid simulation scheme, as described in
Bennedsen, Lunde, Pakkannen (2017) <arXiv:1507.03004v4>, as well as functions to fit BSS processes
to data, and functions to estimate the stochastic volatility process of a BSS process.

Title: Asymptotic covariance matrices of some BSS mixing and unmixing
Author: Jari Miettinen, Klaus Nordhausen, Hannu Oja, Sara Taskinen
Description: The package includes functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: adaptive FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available.

Title: Asymptotic Covariance Matrices of Some BSS Mixing and Unmixing
Description: Functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available.

Description: Functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric and squared symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available.

Description: Functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric and squared symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available. For details, see Miettinen et al. (2015) <doi:10.1214/15-STS520>, Miettinen et al. (2016) <doi:10.1111/jtsa.12159>, Miettinen et al. (2017) <doi:10.1016/j.sigpro.2016.08.028>, Miettinen et al. (2017) <doi:10.18637/jss.v076.i02>, and references therein.