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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.

Versions across snapshots

VersionRepositoryFileSize
0.10.0 rolling linux/jammy R-4.5 templateICAr_0.10.0.tar.gz 472.7 KiB
0.10.0 rolling linux/noble R-4.5 templateICAr_0.10.0.tar.gz 472.6 KiB
0.10.0 rolling source/ R- templateICAr_0.10.0.tar.gz 123.5 KiB
0.10.0 latest linux/jammy R-4.5 templateICAr_0.10.0.tar.gz 472.7 KiB
0.10.0 latest linux/noble R-4.5 templateICAr_0.10.0.tar.gz 472.6 KiB
0.10.0 latest source/ R- templateICAr_0.10.0.tar.gz 123.5 KiB
0.10.0 2026-04-26 source/ R- templateICAr_0.10.0.tar.gz 123.5 KiB
0.10.0 2026-04-23 source/ R- templateICAr_0.10.0.tar.gz 123.5 KiB
0.10.0 2026-04-09 windows/windows R-4.5 templateICAr_0.10.0.zip 481.0 KiB
0.9.1 2025-04-20 source/ R- templateICAr_0.9.1.tar.gz 122.3 KiB

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