mirt1.43 package

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 function...

Create a user defined item with correct generic functions

Differential item functioning statistics

Class "DiscreteClass"

Draw plausible parameter instantiations from a given model

Differential Response Functioning statistics

Differential test functioning statistics

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...

Generalized item difficulty summaries

Imputing plausible data for missing values

Item fit statistics

Parametric smoothed regression lines for item response probability fun...

Function to calculate item information

Displays item surface and information plots

Generic item summary statistics

Score a test by converting response patterns to binary data

Lagrange test for freeing parameters

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

Extract log-likelihood

Compute the M2 model fit statistic

Function to calculate the marginal reliability

Compute multidimensional difficulty index

Multidimensional discrete item response theory

Compute multidimensional discrimination index

Full information maximum likelihood estimation of IRT models.

Specify model information

Full-Information Item Factor Analysis (Multidimensional Item Response ...

Define a parallel cluster object to be used in internal functions

Class "MixedClass"

Mixed effects modeling for MIRT models

Class "MixtureClass"

Convert an estimated mirt model to a data.frame

Multiple Group Estimation

Class "MultipleGroupClass"

Compute numerical derivatives

Person fit statistics

Compute profiled-likelihood (or posterior) confidence intervals

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

Model-based Reliable Change Index

Translate mirt parameters into suitable structure for plink package

Remap item categories to have integer distances of 1

Compute model residuals

Reverse score one or more items from a response matrix

RMSD effect size statistic to quantify category-level DIF

Second-order test of convergence

Show model object

(Generalized) Simultaneous Item Bias Test (SIBTEST)

Simulate response patterns

Class "SingleGroupClass"

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