Package: BayesBrainMap 0.2.0

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.

Authors:Amanda Mejia [aut, cre], Damon Pham [aut], Nohelia Da Silva [ctb]

BayesBrainMap_0.2.0.tar.gz
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manual.pdf |manual.html✨
card.svg |card.png
BayesBrainMap/json (API)
NEWS

# Install 'BayesBrainMap' in R:
install.packages('BayesBrainMap', repos = c('https://mandymejia.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mandymejia/bayesbrainmap/issues

On CRAN:

Conda:

4.74 score 2 stars 12 scripts 217 downloads 6 exports 17 dependencies

Last updated from:343e939bfa. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK177
source / vignettesOK207
linux-release-x86_64OK181
macos-release-arm64OK142
macos-oldrel-arm64OK112
windows-develOK147
windows-releaseOK114
windows-oldrelOK121
wasm-releaseOK129

Exports:BrainMapengagementsestimate_priorexport_priorfit_BBMid_engagements

Dependencies:abindclasscodetoolsDEoptimRe1071fMRIscrubfMRItoolsforeachiteratorslatticeMASSMatrixmatrixStatspeselproxyrobustbaseSQUAREM

Readme and manuals

Help Manual

Help pageTopics
Bdiag m2bdiag_m2
BrainMapBrainMap
Cholesky-based FC samplingChol_samp_fun
EM Algorithms for Bayesian brain mapsEM_BBM.independent EM_BBM.spatial
engagementsengagements
Universally estimate IW dof parameter nu based on method of moments, so that no empirical variance is under-estimatedestimate_nu
Estimate IW dof parameter nu based on method of momentsestimate_nu_matrix
Estimate priorestimate_prior estimate_prior.cifti estimate_prior.gifti estimate_prior.nifti
Estimation of effective sample sizeestimate.ESS
Export priorexport_prior
Bayesian brain mappingfit_BBM
Engagements of (spatial) Bayesian brain mappingid_engagements
Compute theoretical Inverse-Wishart variance of covariance matrix elementsIW_var
Compute theoretical Inverse-Wishart variance of correlation matrix elementsIW_var_cor
Compute likelihood in SPDE model for ESS estimationlik
Plot engagementsplot.bMap_eng.cifti
Plot fit_BBM estiamteplot.bMap.cifti
Plot priorplot.bMap.matrix
Plot priorplot.bMap.nifti
Plot priorplot.prior.cifti
Plot priorplot.prior.gifti
Plot priorplot.prior.matrix
Plot priorplot.prior.nifti
Estimate residual autocorrelation for prewhiteningpw_estimate
Summarize a '"bMap_eng.cifti"' objectprint.bMap_eng.cifti print.summary.bMap_eng.cifti summary.bMap_eng.cifti
Summarize a '"bMap_eng.matrix"' objectprint.bMap_eng.matrix print.summary.bMap_eng.matrix summary.bMap_eng.matrix
Summarize a '"bMap_eng.nifti"' objectprint.bMap_eng.nifti print.summary.bMap_eng.nifti summary.bMap_eng.nifti
Summarize a '"bMap.cifti"' objectprint.bMap.cifti print.summary.bMap.cifti summary.bMap.cifti
Summarize a '"bMap.matrix"' objectprint.bMap.matrix print.summary.bMap.matrix summary.bMap.matrix
Summarize a '"bMap.nifti"' objectprint.bMap.nifti print.summary.bMap.nifti summary.bMap.nifti
Summarize a '"prior.cifti"' objectprint.prior.cifti print.summary.prior.cifti summary.prior.cifti
Summarize a '"prior.gifti"' objectprint.prior.gifti print.summary.prior.gifti summary.prior.gifti
Summarize a '"prior.matrix"' objectprint.prior.matrix print.summary.prior.matrix summary.prior.matrix
Summarize a '"prior.nifti"' objectprint.prior.nifti print.summary.prior.nifti summary.prior.nifti
Parameter Estimates in EM Algorithm for Bayesian brain mapUpdateTheta_BBM UpdateTheta_iBM
Compute the error between empirical and theoretical variance of covariance matrix elementsvar_sq_err
Compute the overall error between empirical and theoretical variance of CORRELATION matrix elementsvar_sq_err_constrained