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BayesBrainMap  

Estimate Brain Networks and Connectivity with Population-Derived Priors
View on CRAN: Click here


Download and install BayesBrainMap package within the R console
Install from CRAN:
install.packages("BayesBrainMap")

Install from Github:
library("remotes")
install_github("cran/BayesBrainMap")

Install by package version:
library("remotes")
install_version("BayesBrainMap", "0.2.0")



Attach the package and use:
library("BayesBrainMap")
Maintained by
Amanda Mejia
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-07-04
Latest Update: 2025-07-04
Description:
Implements Bayesian brain mapping models, including the prior ICA (independent components analysis) model proposed in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638> and the spatial prior 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.
How to cite:
Amanda Mejia (2025). BayesBrainMap: Estimate Brain Networks and Connectivity with Population-Derived Priors. R package version 0.2.0, https://cran.r-project.org/web/packages/BayesBrainMap. Accessed 24 Oct. 2026.
Previous versions and publish date:
0.1.3 (2025-07-04 20:40), 0.2.0 (2026-02-03 10:00), (2026-10-02 11:40)
Other packages that cited BayesBrainMap R package
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Other R packages that BayesBrainMap depends, imports, suggests or enhances
Complete documentation for BayesBrainMap
Functions, R codes and Examples using the BayesBrainMap R package
Full BayesBrainMap package functions and examples
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