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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
# BayesfMRI
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[](https://github.com/mandymejia/BayesfMRI/actions)
[](https://app.codecov.io/gh/mandymejia/BayesfMRI?branch=master)
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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.
<!-- * `BayesGLM_vol` - implements `BayesGLM` on NIFTI subcortical voxel fMRI data -->
## Citation
If you use `BayesfMRI` please cite the following papers:
| Name | APA Citation |
| ------------- | ------------- |
| [Spatial Bayesian GLM](https://doi.org/10.1080/01621459.2019.1611582) | 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](https://doi.org/10.1016/j.neuroimage.2022.118908) | 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:
``` r
citation("BayesfMRI")
```
## Important Note on Dependencies:
`BayesfMRI` depends on the `ciftiTools` package, which requires an installation of Connectome Workbench. It can be installed from the [HCP website](https://www.humanconnectome.org/software/get-connectome-workbench).
<!-- By default, the spatial Bayesian model in `BayesGLM` is implemented using an expectation-maximization algorithm written in C++. To instead use the INLA package, set `EM=FALSE`. The INLA package will be required, as well as an INLA-PARDISO license for computational efficiency. -->
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](https://www.r-inla.org/download-install). **Note: INLA must be installed before installing `BayesfMRI`.**
On Mac platforms, an installation of [Xcode](https://mac.r-project.org/tools/) is necessary to build the C++ code included in `BayesfMRI`.
<!--An INLA-PARDISO license is also required for computational efficiency. To obtain an INLA-PARDISO license, run `inla.pardiso()` in R after running `library(INLA)`. Once you obtain a license, point to it using `INLA::inla.setOption(pardiso.license = "pardiso.lic")` followed by `INLA::inla.pardiso.check()` to ensure that PARDISO is successfully installed and running.
-->