hyemin-han Goto Github PK

bayesfactorfmri

BayesFactorFMRI is a tool developed with R and Python to allow neuroimaging researchers to conduct Bayesian second-level analysis of fMRI data and Bayesian meta-analysis of fMRI images with multiprocessing.

bayesfactorfmri_twot

Two-group t-test based on BayesFactorFMRI

bfmeta

BFMeta program

covidistress2_belief_scales

Testing belief-related scales for COVIDiSTRESS2 dataset

covidistress2_compliance

COVIDiSTRESS 2 Compliance paper

covidistress2_stress

covidistress2_vaccine

explore_mixed_models

Code to explore all possible combinations of mixed models

markov-learning

Markov learning classes

prior-adjustment-bayesfactorfmri

Adjusting prior distributions for Bayesian second-level fMRI analysis

prior-adjustment-cbm

Prior adjustment with coordinate-based meta-analysis for voxelwise Bayesian second-level fMRI analysis

r_alignment_test

tensorflow_intervention

Predicting outcomes of educational interventions before investing in large-scale implementation efforts in school settings is essential for educational policy-making. However, due to time and resource limitations, conducting longitudinal, large-scale experiments testing outcomes of interventions in authentic settings is difficult. Here, we introduce the deep learning method as a way to address this issue and illustrate the use of the deep learning method for the prediction of intervention outcomes through a MATLAB implementation. The presented deep learning method extracts predictable patterns from an empirical dataset to simulate large-scale intervention outcomes. Findings from our simulations suggest that the deep learning applied simulation model can predict intervention outcomes significantly more accurately compared to the traditional regression analysis methods.