Compute the M2 model fit statistic
Item fit statistics
Function to calculate expected value of item
Differential item functioning statistics
Create a user defined item with correct generic functions
Description of Science data
Function to calculate expected test score
Description of Bock 1997 data
Calculate bootstrapped standard errors for estimated models
Collapse values from multiple imputation draws
Class "MultipleGroupClass"
Differential test functioning statistics
Compute multidimensional discrimination index
Extract parameter variance covariance matrix
Class "SingleGroupClass"
Plot various test-implied functions from models
Full-Information Item Bi-factor and Two-Tier Analysis
Function to calculate probability trace lines
Description of deAyala data
Function to calculate the empirical (marginal) reliability
Imputing plausible data for missing values
Compute numerical derivatives
Compute factor score estimates (a.k.a, ability estimates, latent trait estimates, etc)
Function to calculate item information
Multiple Group Estimation
Extract an item object from mirt objects
Lagrange test for freeing parameters
Compute model residuals
Mixed effects modeling for MIRT models
Extract various elements from estimated model objects
Description of SAT12 data
Score a test by converting response patterns to binary data
Class "MixedClass"
Extract log-likelihood
Compare nested models with likelihood-based statistics
Show model object
Convert an estimated mirt model to a data.frame
Description of LSAT7 data
Simultaneous Item Bias Test (SIBTEST)
Empirical effect sizes based on latent trait estimates
Compute profiled-likelihood (or posterior) confidence intervals
Define a parallel cluster object to be used in internal functions
Full information maximum likelihood estimation of IRT models.
Function to generate empirical unidimensional item and test plots
Compute multidimensional difficulty index
Summary of model object
Multidimensional discrete item response theory
Class "DiscreteClass"
Print the model objects
Function to calculate the marginal reliability
Simulate response patterns
Parametric smoothed regression lines for item response probability functions
Compute posterior estimates of random effect
Full-Information Item Factor Analysis (Multidimensional Item Response
Theory)
Displays item surface and information plots
Expand summary table of patterns and frequencies
Description of LSAT6 data
Person fit statistics
Specify model loadings
Function to calculate test information
Extract raw coefs from model object
Function to calculate the area under a selection of information curves
Compute latent regression fixed effect expected values
Extract a group from a multiple group mirt object
Wald statistics for mirt models