BayesBrainMap: Estimate Brain Networks and Connectivity with Population-Derived
Priors
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.
| Version: |
0.2.0 |
| 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) |
| Published: |
2026-02-03 |
| DOI: |
10.32614/CRAN.package.BayesBrainMap |
| Author: |
Amanda Mejia [aut, cre],
Damon Pham  [aut],
Nohelia Da Silva [ctb] |
| Maintainer: |
Amanda Mejia <mandy.mejia at gmail.com> |
| BugReports: |
https://github.com/mandymejia/BayesBrainMap/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/mandymejia/BayesBrainMap |
| NeedsCompilation: |
no |
| Additional_repositories: |
https://inla.r-inla-download.org/R/testing |
| Citation: |
BayesBrainMap citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
BayesBrainMap results |
Documentation:
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