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: 0.10.0 Depends: R (≥ 3.6.0) Imports: abind, fMRItools (≥ 0.5.3), fMRIscrub (≥ 0.14.5), foreach, ica, Matrix, matrixStats, methods, pesel, SQUAREM, stats, utils Suggests: ciftiTools (≥ 0.13.2), excursions, RNifti, oro.nifti, gifti, covr, parallel, doParallel, knitr, rmarkdown, INLA, testthat (≥ 3.0.0) Published: 2025-05-19 DOI: 10.32614/CRAN.package.templateICAr Author: Amanda Mejia [aut, cre], Damon Pham [aut], Daniel Spencer [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: no Additional_repositories: https://inla.r-inla-download.org/R/testing Citation: templateICAr citation info Materials: README NEWS CRAN checks: templateICAr resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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