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)

Latent Space Measurement Models

• OSF-based Repository for various latent space measurement models

• https://osf.io/zaxqd/ 

• Currently provides R and stan programs for 

  • Latent Space Diffusion Item Response Theory (LSDIRT) Model

  • Latent Space Confirmatory Factor Analysis (LSCFA) Model

  • Latent Space Graded Response Model (LSGRM)

• Will be featured soon: Multidimensional Latent Space Item Response Models (Kang & Jeon, 2025, Psychometrika)

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)