hyemin-han

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.

BFMeta

BFMeta program

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.

hyemin-han/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.

hyemin-han/BFMeta

BFMeta program

hyemin-han/COVIDiSTRESS2_Stress

hyemin-han/COVIDiSTRESS2_Vaccine

hyemin-han/Explore_Mixed_Models

Code to explore all possible combinations of mixed models

hyemin-han/Prior-Adjustment-CBM

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

hyemin-han/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.

hyemin-han/BayesFactorFMRI_TWOT

Two-group t-test based on BayesFactorFMRI

hyemin-han/COVIDiSTRESS2_belief_scales

Testing belief-related scales for COVIDiSTRESS2 dataset

hyemin-han/COVIDiSTRESS2_Compliance

COVIDiSTRESS 2 Compliance paper

hyemin-han/Markov-Learning

Markov learning classes

hyemin-han/Prior-Adjustment-BayesFactorFMRI

Adjusting prior distributions for Bayesian second-level fMRI analysis

hyemin-han/R_Alignment_Test