Package: BayesfMRI 0.10.1
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:
BayesfMRI_0.10.1.tar.gz
BayesfMRI_0.10.1.zip(r-4.5)BayesfMRI_0.10.1.zip(r-4.4)BayesfMRI_0.10.1.zip(r-4.3)
BayesfMRI_0.10.1.tgz(r-4.5-x86_64)BayesfMRI_0.10.1.tgz(r-4.5-arm64)BayesfMRI_0.10.1.tgz(r-4.4-x86_64)BayesfMRI_0.10.1.tgz(r-4.4-arm64)BayesfMRI_0.10.1.tgz(r-4.3-x86_64)BayesfMRI_0.10.1.tgz(r-4.3-arm64)
BayesfMRI_0.10.1.tar.gz(r-4.5-noble)BayesfMRI_0.10.1.tar.gz(r-4.4-noble)
BayesfMRI_0.10.1.tgz(r-4.4-emscripten)BayesfMRI_0.10.1.tgz(r-4.3-emscripten)
BayesfMRI.pdf |BayesfMRI.html✨
BayesfMRI/json (API)
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
Last updated 26 days agofrom:b0b5c759bc. Checks:11 OK, 1 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 09 2025 |
| R-4.5-win-x86_64 | OK | Mar 09 2025 |
| R-4.5-mac-x86_64 | OK | Mar 09 2025 |
| R-4.5-mac-aarch64 | OK | Mar 09 2025 |
| R-4.5-linux-x86_64 | OK | Mar 09 2025 |
| R-4.4-win-x86_64 | OK | Mar 09 2025 |
| R-4.4-mac-x86_64 | OK | Mar 09 2025 |
| R-4.4-mac-aarch64 | OK | Mar 09 2025 |
| R-4.4-linux-x86_64 | OK | Mar 09 2025 |
| R-4.3-win-x86_64 | NOTE | Mar 09 2025 |
| R-4.3-mac-x86_64 | OK | Mar 09 2025 |
| R-4.3-mac-aarch64 | OK | Mar 09 2025 |
Exports:activationsBayesGLMBayesGLM2do_QCfit_bayesglmid_activationsmake_meshprevalencescale_BOLDvertex_areasvol2spde
Dependencies:abindbackportsbase64encbitopsbootbroombslibcachemcarcarDataciftiToolsclassclassIntclicodetoolscolorspacecowplotcpp11DBIDerivdigestdoBydotCall64dplyre1071evaluateexcursionsfansifarverfastmapfieldsfmesherfMRItoolsfontawesomeforeachFormulafsgenericsggplot2giftigluegtablehighrhtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelifecyclelme4magrittrmapsMASSMatrixMatrixModelsmatrixStatsmemoisemgcvmicrobenchmarkmimeminqamodelrmunsellnlmenloptrnnetnumDerivoro.niftipbkrtestpillarpkgconfigproxypurrrquantregR.methodsS3R.ooR.utilsR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrglrlangrmarkdownRNiftis2sassscalessfspspamSparseMstringistringrsurvivaltibbletidyrtidyselecttinytexunitsutf8vctrsviridisLitewithrwkxfunxml2yaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| BayesfMRI: Spatial Bayesian Methods for Task Functional MRI Studies | BayesfMRI-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 activations | activations id_activations |
| aic | aic_Param |
| ar_order | ar_order_Param |
| ar_smooth | ar_smooth_Param |
| Bayes | Bayes_Param |
| BayesGLM for CIFTI | BayesGLM |
| Group-level Bayesian GLM | BayesGLM2 |
| BOLD | BOLD_Param_BayesGLM |
| brainstructures | brainstructures_Param_BayesGLM |
| buffer | buffer_Param |
| Connectome Workbench | Connectome_Workbench_Description |
| contrasts | contrasts_Param |
| design | design_Param_BayesGLM |
| Mask out invalid data | do_QC |
| EM | EM_Param |
| emTol | emTol_Param |
| faces | faces_Param |
| field_names | field_names_Param |
| fit_bayesglm | fit_bayesglm |
| hpf | hpf_Param_BayesGLM |
| INLA | INLA_Description |
| INLA Latent Fields | INLA_Latent_Fields_Limit_Description |
| Make Mesh | make_mesh |
| mask: vertices | mask_Param_vertices |
| max_threads | max_threads_Param |
| mean and variance tolerance | mean_var_Tol_Param |
| mesh: either | mesh_Param_either |
| mesh: INLA only | mesh_Param_inla |
| n_threads | n_threads_Param |
| nbhd_order | nbhd_order_Param |
| nuisance | nuisance_Param_BayesGLM |
| S3 method: use 'view_xifti' to plot a '"act_BGLM"' object | plot.act_BGLM |
| S3 method: use 'view_xifti' to plot a '"BGLM"' object | plot.BGLM |
| S3 method: use 'view_xifti' to plot a '"BGLM2"' object | plot.BGLM2 |
| S3 method: use 'view_xifti' to plot a '"prev_BGLM"' object | plot.prev_BGLM |
| Activations prevalence. | prevalence |
| resamp_res | resamp_res_Param_BayesGLM |
| return_INLA | return_INLA_Param |
| Scale the BOLD timeseries | scale_BOLD |
| scale_BOLD | scale_BOLD_Param |
| scrub | scrub_Param_BayesGLM |
| seed | seed_Param |
| session_names | session_names_Param |
| Summarize a '"act_BGLM"' object | print.act_BGLM print.summary.act_BGLM summary.act_BGLM |
| Summarize a '"act_fit_bglm"' object | print.act_fit_bglm print.summary.act_fit_bglm summary.act_fit_bglm |
| Summarize a '"BGLM"' object | print.BGLM print.summary.BGLM summary.BGLM |
| Summarize a '"BGLM2"' object | print.BGLM2 print.summary.BGLM2 summary.BGLM2 |
| Summarize a '"fit_bglm"' object | print.fit_bglm print.summary.fit_bglm summary.fit_bglm |
| Summarize a '"fit_bglm2"' object | print.fit_bglm2 print.summary.fit_bglm2 summary.fit_bglm2 |
| Summarize a '"prev_BGLM"' object | print.prev_BGLM print.summary.prev_BGLM summary.prev_BGLM |
| Summarize a '"prev_fit_bglm"' object | print.prev_fit_bglm print.summary.prev_fit_bglm summary.prev_fit_bglm |
| surfaces | surfaces_Param_BayesGLM |
| TR | TR_Param_BayesGLM |
| trim_INLA | trim_INLA_Param |
| verbose | verbose_Param |
| Surface area of each vertex | vertex_areas |
| vertices | vertices_Param |
| Construct a triangular mesh from a 3D volumetric mask | vol2spde |
