templateICAr

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.

Last 30 days

This package has been downloaded 22,534 times in the last 30 days. The academic equivalent of having a dedicated subreddit. There are fans, and maybe even a few trolls! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 763 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 134,382 times in the last 365 days. This is the kind of download count that makes grant committees nod approvingly. A job well done, even the stoic reviewers might be impressed! The day with the most downloads was Nov 23, 2024 with 915 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.