The BayesfMRI
R package includes the main function BayesGLM
, which
implements a spatial Bayesian GLM for task fMRI. It also contains a
wrapper function BayesGLM_cifti
, for CIFTI cortical surface fMRI data.
If you use BayesfMRI
please cite the following papers:
| Name | APA Citation | |----------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Spatial Bayesian GLM | Mejia, A. F., Yue, Y., Bolin, D., Lindgren, F., & Lindquist, M. A. (2020). A Bayesian general linear modeling approach to cortical surface fMRI data analysis. Journal of the American Statistical Association, 115(530), 501-520. | | Multi-session Spatial Bayesian GLM | Spencer, D., Yue, Y. R., Bolin, D., Ryan, S., & Mejia, A. F. (2022). Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups. NeuroImage, 249, 118908. |
You can also obtain citation information from within R like so:
citation("BayesfMRI")
You can install BayesfMRI
from CRAN
with:
install.packages("BayesfMRI")
See this link to view the tutorial vignette.
BayesfMRI
depends on the ciftiTools
package, which requires an
installation of Connectome Workbench. It can be installed from the HCP
website.
The INLA package is required, which, due to a CRAN policy, will not be
installed automatically. You can obtain it by running
install.packages("INLA",repos=c(getOption("repos"),INLA="https://inla.r-inla-download.org/R/stable"), dep=FALSE)
.
For more information, see the INLA
website. Note: INLA must be
installed before installing BayesfMRI
.
On Mac platforms, an installation of
Xcode is necessary to build the C++
code included in BayesfMRI
.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.