Package: BayesfMRI 0.8.0

BayesfMRI: Spatial Bayesian Methods for Task Functional MRI Studies

Performs a spatial Bayesian general linear model (GLM) for task functional magnetic resonance imaging (fMRI) data on the cortical surface. Additional models include group analysis and inference to detect thresholded areas of activation. Includes direct support for the 'CIFTI' neuroimaging file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F. Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582> and D. Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) <doi:10.1016/j.neuroimage.2022.118908>.

Authors:Amanda Mejia [aut, cre], Damon Pham [ctb], David Bolin [ctb], Yu Yue [ctb], Daniel Spencer [aut], Sarah Ryan [ctb]

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NEWS

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

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

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

cpp

5.63 score 25 stars 19 scripts 299 downloads 22 exports 117 dependencies

Last updated 8 months agofrom:8250b37b00. Checks:6 OK, 5 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 21 2025
R-4.5-win-x86_64NOTEFeb 21 2025
R-4.5-mac-x86_64NOTEFeb 21 2025
R-4.5-mac-aarch64NOTEFeb 21 2025
R-4.5-linux-x86_64NOTEFeb 21 2025
R-4.4-win-x86_64OKFeb 21 2025
R-4.4-mac-x86_64OKFeb 21 2025
R-4.4-mac-aarch64OKFeb 21 2025
R-4.3-win-x86_64NOTEFeb 21 2025
R-4.3-mac-x86_64OKFeb 21 2025
R-4.3-mac-aarch64OKFeb 21 2025

Exports:activationsBayesGLMBayesGLM_groupBayesGLM2cderivfit_bayesglmHRF_calcHRF_mainHRF96id_activationsmake_designmake_maskmake_meshmultiGLMmultiGLM_funplot_designplot_design_imageplot_design_lineprevalencescale_BOLDvertex_areasvol2spde

Dependencies:abindbackportsbase64encbitopsbootbroombslibcachemcarcarDataciftiToolsclassclassIntclicodetoolscolorspacecowplotcpp11DBIDerivdigestdoBydotCall64dplyre1071evaluateexcursionsfansifarverfastmapfieldsfmesherfMRItoolsfontawesomeforeachFormulafsgenericsggplot2giftigluegtablehighrhtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelifecyclelme4magrittrmapsMASSMatrixMatrixModelsmatrixStatsmemoisemgcvmicrobenchmarkmimeminqamodelrmunsellnlmenloptrnnetnumDerivoro.niftipbkrtestpillarpkgconfigproxypurrrquantregR.methodsS3R.ooR.utilsR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrglrlangrmarkdownRNiftis2sassscalessfspspamSparseMstringistringrsurvivaltibbletidyrtidyselecttinytexunitsutf8vctrsviridisLitewithrwkxfunxml2yaml

Readme and manuals

Help Manual

Help pageTopics
BayesfMRI: Spatial Bayesian Methods for Task Functional MRI StudiesBayesfMRI-package BayesfMRI
Perform the EM algorithm of the Bayesian GLM fitting.findTheta
Get the prewhitening matrix for a single data location.getSqrtInvCpp
Find the initial values of kappa2 and phi.initialKP
Find the log of the determinant of Q_tilde.logDetQt
Identify field activationsactivations id_activations
aicaic_Param
ar_orderar_order_Param
ar_smoothar_smooth_Param
BayesBayes_Param
BayesGLM for CIFTIBayesGLM
Group-level Bayesian GLMBayesGLM2 BayesGLM_group
BOLDBOLD_Param_BayesGLM
brainstructuresbrainstructures_Param_BayesGLM
bufferbuffer_Param
Central derivativecderiv
Connectome WorkbenchConnectome_Workbench_Description
contrastscontrasts_Param
designdesign_Param_BayesGLM
EMEM_Param
emTolemTol_Param
facesfaces_Param
field_namesfield_names_Param
fit_bayesglmfit_bayesglm
hpfhpf_Param_BayesGLM
Canonical HRF and DerivativesHRF_calc
Canonical (double-gamma) HRFHRF_main
Canonical (double-gamma) HRF (old one from SPM96, Glover)HRF96
INLAINLA_Description
INLA Latent FieldsINLA_Latent_Fields_Limit_Description
Make design matrixmake_design
Mask out invalid datamake_mask
Make Meshmake_mesh
mask: verticesmask_Param_vertices
max_threadsmax_threads_Param
mean and variance tolerancemean_var_Tol_Param
mesh: eithermesh_Param_either
mesh: INLA onlymesh_Param_inla
multiGLM for CIFTImultiGLM
multiGLM0multiGLM_fun
n_threadsn_threads_Param
nbhd_ordernbhd_order_Param
nuisancenuisance_Param_BayesGLM
Plot design matrixplot_design plot_design_image plot_design_line
S3 method: use 'view_xifti' to plot a '"act_BGLM"' objectplot.act_BGLM
S3 method: use 'view_xifti' to plot a '"BGLM"' objectplot.BfMRI_design
S3 method: use 'view_xifti' to plot a '"BGLM"' objectplot.BGLM
S3 method: use 'view_xifti' to plot a '"BGLM2"' objectplot.BGLM2
S3 method: use 'view_xifti' to plot a '"prev_BGLM"' objectplot.prev_BGLM
Activations prevalence.prevalence
resamp_resresamp_res_Param_BayesGLM
return_INLAreturn_INLA_Param
Scale the BOLD timeseriesscale_BOLD
scale_BOLDscale_BOLD_Param
seedseed_Param
session_namessession_names_Param
Summarize a '"act_BGLM"' objectprint.act_BGLM print.summary.act_BGLM summary.act_BGLM
Summarize a '"act_fit_bglm"' objectprint.act_fit_bglm print.summary.act_fit_bglm summary.act_fit_bglm
Summarize a '"BfMRI_design"' objectprint.BfMRI_design print.summary.BfMRI_design summary.BfMRI_design
Summarize a '"BGLM"' objectprint.BGLM print.summary.BGLM summary.BGLM
Summarize a '"BGLM2"' objectprint.BGLM2 print.summary.BGLM2 summary.BGLM2
Summarize a '"fit_bglm"' objectprint.fit_bglm print.summary.fit_bglm summary.fit_bglm
Summarize a '"fit_bglm2"' objectprint.fit_bglm2 print.summary.fit_bglm2 summary.fit_bglm2
Summarize a '"prev_BGLM"' objectprint.prev_BGLM print.summary.prev_BGLM summary.prev_BGLM
Summarize a '"prev_fit_bglm"' objectprint.prev_fit_bglm print.summary.prev_fit_bglm summary.prev_fit_bglm
surfacessurfaces_Param_BayesGLM
TRTR_Param_BayesGLM
trim_INLAtrim_INLA_Param
verboseverbose_Param
Surface area of each vertexvertex_areas
verticesvertices_Param
Construct a triangular mesh from a 3D volumetric maskvol2spde