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 22,644 times in the last 30 days. That's enough downloads to make it mildly famous in niche technical communities. A badge of honor! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 632 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 132,871 times in the last 365 days. That's a whole lot of downloads. Somewhere, a librarian is trying to figure out why more bandwidth is needed. 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.