psychometrics package, including MIRT(multidimension item response theory), IRT(item response theory),GRM(grade response theory),CAT(computerized adaptive testing), CDM(cognitive diagnostic model), FA(factor analysis), SEM(Structural Equation Modeling) .

Cognitive Diagnosis Modeling

R package for the Bayesian estimation of diagnostic classification models using Stan

R package for fitting hidden Markov cognitive diagnosis models for learning.

Supplementary data package for the edm package

Jointly model the accuracy of cognitive responses and item choices within a bayesian hierarchical framework as described by Culpepper and Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package contains the datasets used within the analysis of the paper.

Perform a Bayesian estimation of the Exploratory Deterministic Input, Noisy “And” Gate (EDINA) cognitive diagnostic model described by Chen et al. (2018) <doi:10.1007/s11336-017-9579-4>

Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> four parameter IRT model with lower and upper asymptotes using Bayesian formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.

Modeling framework for Exploratory Diagnostic Models (EDM)

An overview of the ECDM modeling framework.

Mixture of Cognitive Diagnosis Models

Simulate cognitive diagnostic model data for Deterministic Input, Noisy "And" Gate (DINA) and reduced Reparameterized Unified Model (rRUM) from Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>, Culpepper (2015) <doi:10.3102/1076998615595403>, and de la Torre (2009) <doi:10.3102/1076998607309474>.

The goal of rrum is to provide an implementation of Gibbs sampling algorithm for Bayesian Estimation of reduced Reparametrized Unifed Model (rRUM), described by Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>.

Web interface powered by shiny for modeling with exploratory cognitive diagnostic models (ECDMs)

🚨[WIP]🚨Classes and Algorithms used across Exploratory Diagnostic Modeling Framework