Multidimensional item response theory

Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. **Superseded by the models-by-example repo**.

Computerized Adaptive Testing with Multidimensional Item Response Theory

idealstan offers item-response theory (IRT) ideal-point estimation for binary, ordinal, counts and continuous responses with time-varying and missing-data inference. Latent space model also included. Full and approximate Bayesian sampling with 'Stan' (www.mc-stan.org).

◽ <- ⚪ Structural Equation Modeling from a broader context.

IRT ideal point models in R using Stan

Dynamic estimation of group-level opinion in R

IR-Tree Modeling in mirt, Mplus, or TAM

The goal of psymetrics is to provide a unified set of tools to extract, compare, format, and visualize psychometric model fit indices and parameter estimates, offering a consistent workflow across popular R packages such as lavaan, psych, and mirt.

tmt package for the application of the conditional maximum likelihood (CML) estimation in multistage designs

Supplementary code to the paper "Computational methods for Bayesian semiparametric Item Response Theory models"

Person Parameter estimation (1,2,3,4PL model) and GPCM

A (ggplot2 wrapper) R package for Item Response Theory model visualization

Repositorio con el material del taller "Introducción a la Modelización Psicómetrica con IRT usando R" dictado por Dr. Ramsés Vázquez Lira en el Instituto Nacional para la Evaluación Educativa (INEE) como parte de la clase "Seminario de Medición y Evaluación en Educación" impartida en conjunto con la Facultad de Psicología, UNAM. Auxiliar: Josué …

Unidimensional IRT models using mirt

regDIF: Regularized Differential Item Functioning

A Wald statistic for group-level cheating detection

Person Parameter estimation (1,2,3,4PL model) and GPCM

An R package for simulating responses and estimating parameters to the Multi-Unidimensional Pairwise Preference (MUPP) Model

Functions to ease the application of various forms of computer adaptive tests.