Perform a Bayesian estimation of the exploratory Sparse Latent Class Model for Binary Data described by Chen, Y., Culpepper, S. A., and Liang, F. (2020) <https://doi.org/10.1007/s11336-019-09693-2>

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>.

Ordinal Higher-Order Exploratory General Diagnostic Models for Polytomous Data described by Culpepper and Balamuta (In Press) <doi:10.1080/00273171.2021.1985949>.

Instant Polychoric and Polyserial Correlation

Perform a Bayesian estimation of the Exploratory reduced Reparameterized Unified Model (errum) described by Culpepper and Chen (2018) <doi:10.3102/1076998618791306>.

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>.

Toolbox to build (color) vision experiments

Joint Maximum Likelihood Estimation for High-Dimensional Item Factor Analysis

Estimate the Deterministic Input, Noisy "And" Gate (DINA) cognitive diagnostic model parameters using the Gibbs sampler described by Culpepper (2015) <doi:10.3102/1076998615595403>.

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