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 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. Note, this package has been
deprecated and superseded by 'BayesBrainMap', which includes model
improvements and new names for the core functions.
| Version: |
0.11.3 |
| 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: |
2026-09-16 |
| 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://cran.r-project.org/package=BayesBrainMap |
| NeedsCompilation: |
no |
| Additional_repositories: |
https://inla.r-inla-download.org/R/testing |
| Citation: |
templateICAr citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
templateICAr results |