templateICAr: Estimate Brain Networks Using Empirical Population Priors

Functions implementing the template ICA model proposed in Mejia et al. (2019) <doi:10.1080/01621459.2019.1679638> and the spatial template ICA model proposed in proposed in Mejia et al. (2020+). For both models, subject-level brain networks are estimated as deviations from known population-level networks, which can be estimated using standard ICA algorithms. Both models employ an expectation-maximization algorithm for estimation of the latent brain networks and unknown model parameters.

Version: 0.3.1
Depends: R (≥ 3.6.0)
Imports: abind, ciftiTools (≥ 0.8.0), excursions, fMRIscrub, ica, Matrix, matrixStats, methods, pesel, SQUAREM, stats
Suggests: RNifti, covr, doParallel, foreach, knitr, rmarkdown, INLA, parallel, testthat (≥ 3.0.0)
Published: 2022-03-09
Author: Amanda Mejia [aut, cre], Damon Pham ORCID iD [aut], 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: no
Additional_repositories: https://inla.r-inla-download.org/R/testing
Citation: templateICAr citation info
Materials: README NEWS
CRAN checks: templateICAr results


Reference manual: templateICAr.pdf


Package source: templateICAr_0.3.1.tar.gz
Windows binaries: r-devel: templateICAr_0.3.1.zip, r-release: templateICAr_0.3.1.zip, r-oldrel: templateICAr_0.3.1.zip
macOS binaries: r-release (arm64): templateICAr_0.3.1.tgz, r-release (x86_64): templateICAr_0.3.1.tgz, r-oldrel: templateICAr_0.3.1.tgz


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