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  [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: |
yes |
| Additional_repositories: |
https://inla.r-inla-download.org/R/testing |
| Citation: |
templateICAr citation info |
| Materials: |
README NEWS |
| CRAN checks: |
templateICAr results |