Programs/Codes

DIRT-RV

• OSF link: https://osf.io/vg2nf/ 

• An R program to fit the Diffusion Item Response Theory Model with Random Variability

• A model to study conditional dependence between responses and response times

• Implements Bayesian estimation based on Differential Evolution Markov Chain Monte Carlo 

• References: 1) Kang, De Boeck, & Ratcliff (2022, Psychometrika), 2) Kang, De Boeck, & Partchev (2022, Intelligence)

Lasso FA NDDM

• github link: https://github.com/MbCN-lab/LassoFANDDM 

• An R program to fit the Neural Drift Diffusion Model with a Factor Analysis Linking Function Regularized by Bayesian Lasso

• Implements Bayesian estimation based on Differential Evolution Markov Chain Monte Carlo 

• Reference: Kang, Yi, & Turner (2022, Psychological Methods)

LSIRM

• In the Supplementary Material of the relevant published paper: Link (See Section S1)

• A Stan code to fit the Latent Space Item Response Model

• Reference: Kang & Jeon (2024, Journal of Intelligence) 

LSDIRT

• In the Supplementary Material of the relevant published paper: Link (See Section S1)

• A Stan code to fit the Latent Space Diffusion Item Response Theory Model

• Latent space modeling integrated with the diffusion item response theory model that decomposes conditional dependence between responses and response times to produce diagnostic evaluation and feedback

• Reference: Kang, Jeon, & Partchev (2023, Psychometrika)