Buy me a coffeefmri Package
Added Sunday Dec 13th, 2020
What is this?Description from site: Contains R-functions to perform an fMRI analysis as described inPolzehl and Tabelow (2019) DOI:10.1007/978-3-030-29184-6,Tabelow et al. (2006) DOI:10.1016/j.neuroimage.2006.06.029,Polzehl et al. (2010) DOI:10.1016/j.neuroimage.2010.04.241,Tabelow and Polzehl (2011) DOI:10.18637/jss.v044.i11.
Cool functions from package:
- read.NIFTI(): Read fMRI data from NIFTI file(s)
- setmask(): Add or replace mask in an fmridata object
- oro2fmri()/fmri2oro(): Convert Between fmridata and oro.nifti
- cutroi(): This functions cuts a region-of-interest (ROI) from input data
- fmri.cluster(): Cluster thresholding
- fmri.design (): Return a design matrix for a linear model with given stimuli and possible polynomial drift terms
- fmri.designG(): This function returns a design matrix for multi-subject fMRI data to fit a Linear Mixed-effects Model (one-stage procedure) with given stimuli, polynomial drift terms and a set of known population parameters
- fmri.lmePar(): Group maps are directly estimated from the BOLD time series data of all subjects using lme from R package nlme to fit a Linear Mixed-effects Model with temporally correlated and heteroscedastic within-subject errors. Voxel-wise regression analysis is accelerated by optional parallel processing using R package parallel
- fmri.metaPar(): Linear Mixed-effects Meta-Analysis model for fMRI data
- fmri.pvalue(): P-values
- plot.fmripvalue(): Visualize fMRI p-value maps