Bayesian Hierarchical Multi-Subject Multiscale Analysis of Functional MRI Data


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Documentation for package ‘BHMSMAfMRI’ version 1.3

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BHMSMA Bayesian hierarchical multi-subject multiscale analysis of functional MRI data
BHMSMAfMRI Bayesian Hierarchical Multi-Subject Multiscale Analysis of Functional MRI Data
fmridata A simulated fMRI data for 3 subjects
glmcoeff Fit GLM to the data time-series and obtain GLM coefficients along with standard error estimates
hyperparamest Get the estimates of the hyperparameters of the BHMSME model along with the estimate of their covariance matrix.
pikljbar Compute the piklj bar values of the BHMSMA model using Newton Cotes algorithm
postglmcoeff Obtain the posterior mean of the GLM coefficients using the posterior mean of the wavelet coefficients.
postgroupcoeff Obtain posterior group coefficients using the BHMSMA methodology.
postsamples Generate samples from the posterior distribution of the GLM coefficients.
postwaveletcoeff Obtain posterior mean and posterior median of the wavelet coefficients using BHMSMA methodology.
read.fmridata Read fMRI data from fMRI image files.
waveletcoeff Apply discrete wavelet transform to the GLM coefficients and obtain the wavelet coefficients.