templateICAr: Estimate Brain Networks and Connectivity with ICA and Empirical Priors

Implements the template ICA (independent components analysis) model proposed in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638> and the spatial template ICA model proposed in proposed in Mejia et al. (2022) <doi:10.1080/10618600.2022.2104289>. Both models estimate subject-level brain as deviations from known population-level networks, which are estimated using standard ICA algorithms. Both models employ an expectation-maximization algorithm for estimation of the latent brain networks and unknown model parameters. Includes direct support for 'CIFTI', 'GIFTI', and 'NIFTI' neuroimaging file formats.

Version: 0.6.4
Depends: R (≥ 3.6.0)
Imports: abind, excursions, expm, fMRItools (≥ 0.2.2), ica, Matrix, matrixStats, methods, pesel, Rcpp, SQUAREM, stats, utils
LinkingTo: RcppEigen, Rcpp
Suggests: ciftiTools, RNifti, oro.nifti, gifti, covr, doParallel, foreach, knitr, rmarkdown, INLA, parallel, testthat (≥ 3.0.0)
Published: 2024-01-17
Author: Amanda Mejia [aut, cre], Damon Pham ORCID iD [aut], Daniel Spencer ORCID iD [ctb], Mary Beth Nebel [ctb]
Maintainer: Amanda Mejia <mandy.mejia at gmail.com>
BugReports: https://github.com/mandymejia/templateICAr/issues
License: GPL-3
URL: https://github.com/mandymejia/templateICAr
NeedsCompilation: yes
Additional_repositories: https://inla.r-inla-download.org/R/testing
Citation: templateICAr citation info
Materials: README NEWS
CRAN checks: templateICAr results

Documentation:

Reference manual: templateICAr.pdf

Downloads:

Package source: templateICAr_0.6.4.tar.gz
Windows binaries: r-devel: templateICAr_0.6.4.zip, r-release: templateICAr_0.6.4.zip, r-oldrel: templateICAr_0.6.4.zip
macOS binaries: r-release (arm64): templateICAr_0.6.4.tgz, r-oldrel (arm64): templateICAr_0.6.4.tgz, r-release (x86_64): templateICAr_0.6.4.tgz
Old sources: templateICAr archive

Reverse dependencies:

Reverse suggests: fMRItools

Linking:

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