Nothing
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.
Getting started
Browse package contents
Package details |
|
|---|---|
| Author | Amanda Mejia [aut, cre], Damon Pham [aut] (<https://orcid.org/0000-0001-7563-4727>), Daniel Spencer [ctb] (<https://orcid.org/0000-0002-9705-3605>), Mary Beth Nebel [ctb] |
| Maintainer | Amanda Mejia <mandy.mejia@gmail.com> |
| License | GPL-3 |
| Version | 0.9.1 |
| URL | https://github.com/mandymejia/templateICAr |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the templateICAr package in your browser
Any scripts or data that you put into this service are public.
