Estimate Brain Networks and Connectivity with Population-Derived Priors R package BayesBrainMap version 0.2.0 Implements Bayesian brain mapping with population-derived priors, including the original model described in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638>, the model with spatial priors described in Mejia et al. (2022) <doi:10.1080/10618600.2022.2104289>, and the model with population-derived priors on functional connectivity described in Mejia et al. (2025) <doi:10.1093/biostatistics/kxaf022>. Population-derived priors are based on templates representing established brain network maps, for example derived from independent component analysis (ICA), parcellations, or other methods.  Model estimation is based on expectation-maximization or variational Bayes algorithms. Includes direct support for 'CIFTI', 'GIFTI', and 'NIFTI' neuroimaging file formats. Software Depends: R (>= 3.6.0) Imports: abind, fMRItools (>= 0.7.1), fMRIscrub (>= 0.14.5), foreach, Matrix, matrixStats, methods, pesel, SQUAREM, stats, utils Suggests: ciftiTools (>= 0.13.2), excursions, RNifti, oro.nifti, gifti, ggplot2, 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>), Nohelia Da Silva [ctb] GPL-3 2026-02-03 application/tgz https://CRAN.R-project.org/package=BayesBrainMap doi:10.32614/CRAN.package.BayesBrainMap