mirt

Description of Bock 1997 data

Differential item functioning statistics

Differential Response Functioning statistics

Differential test functioning statistics

Class "DiscreteClass"

Description of LSAT6 data

Description of LSAT7 data

Compute the M2 model fit statistic

Compute multidimensional difficulty index

Compute multidimensional discrimination index

Class "MixedClass"

Class "MixtureClass"

Class "MultipleGroupClass"

Compute profiled-likelihood (or posterior) confidence intervals

RMSD effect size statistic to quantify category-level DIF

Description of SAT12 data

Simultaneous Item Bias Test (SIBTEST)

Description of Science data

Class "SingleGroupClass"

Compare nested models with likelihood-based statistics

Function to calculate the area under a selection of information curves

Collapse values from multiple imputation draws

Full-Information Item Bi-factor and Two-Tier Analysis

Parametric bootstrap likelihood-ratio test

Calculate bootstrapped standard errors for estimated models

Extract raw coefs from model object

Create a user defined group-level object with correct generic functions

Create a user defined item with correct generic functions

Description of deAyala data

Draw plausible parameter instantiations from a given model

Empirical effect sizes based on latent trait estimates

Function to generate empirical unidimensional item and test plots

Function to calculate the empirical (marginal) reliability

Extract Empirical Estimating Functions

Expand summary table of patterns and frequencies

Function to calculate expected value of item

Function to calculate expected test score

Extract a group from a multiple group mirt object

Extract an item object from mirt objects

Extract various elements from estimated model objects

Fixed-item calibration method

Compute latent regression fixed effect expected values

Compute factor score estimates (a.k.a, ability estimates, latent trait estimates, etc)

Generalized item difficulty summaries

Imputing plausible data for missing values

Parametric smoothed regression lines for item response probability functions

Item fit statistics

Function to calculate item information

Displays item surface and information plots

Score a test by converting response patterns to binary data

Lagrange test for freeing parameters

Convert ordered Likert-scale responses (character or factors) to integers

Extract log-likelihood

Function to calculate the marginal reliability

Multidimensional discrete item response theory

Full-Information Item Factor Analysis (Multidimensional Item Response Theory)

Full information maximum likelihood estimation of IRT models.

Specify model information

Define a parallel cluster object to be used in internal functions

Mixed effects modeling for MIRT models

Convert an estimated mirt model to a data.frame

Multiple Group Estimation

Compute numerical derivatives

Person fit statistics

Plot various test-implied functions from models

Change polytomous items to dichotomous item format

Print the model objects

Print generic for customized data.frame console output

Print generic for customized list console output

Print generic for customized matrix console output

Function to calculate probability trace lines

Compute posterior estimates of random effect

Translate mirt parameters into suitable structure for plink package

Remap item categories to have integer distances of 1

Compute model residuals

Show model object

Simulate response patterns

Summary of model object

Function to calculate test information

Create all possible combinations of vector input

Convert traditional IRT metric into slope-intercept form used in mirt

Extract parameter variance covariance matrix

Wald statistics for mirt models