Estimate Brain Networks and Connectivity with ICA and Empirical Priors R package templateICAr version 0.10.0 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. Software 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) Amanda Mejia <mandy.mejia@gmail.com> Comprehensive R Archive Network (CRAN) Amanda Mejia [aut, cre], Damon Pham [aut] (ORCID: <https://orcid.org/0000-0001-7563-4727>), Daniel Spencer [ctb] (ORCID: <https://orcid.org/0000-0002-9705-3605>), Mary Beth Nebel [ctb] GPL-3 2025-05-19 application/tgz https://CRAN.R-project.org/package=templateICAr doi:10.32614/CRAN.package.templateICAr