Learn R Programming

⚠️There's a newer version (1.42) of this package.Take me there.

mirt (version 1.17.1)

Multidimensional Item Response Theory

Description

Analysis of dichotomous and polytomous response data using unidimensional and multidimensional latent trait models under the Item Response Theory paradigm. Exploratory and confirmatory models can be estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier analyses are available for modeling item testlets. Multiple group analysis and mixed effects designs also are available for detecting differential item and test functioning as well as modelling item and person covariates.

Copy Link

Version

Install

install.packages('mirt')

Monthly Downloads

6,695

Version

1.17.1

License

GPL (>= 3)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/philchalmers/mirt

Homepage

https://github.com/philchalmers/mirt/wiki

Maintainer

Last Published

April 27th, 2016

Functions in mirt (1.17.1)

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

MultipleGroupClass-class

Class "MultipleGroupClass"

Differential test functioning statistics

Compute multidimensional discrimination index

Extract parameter variance covariance matrix

SingleGroupClass-class

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

numerical_deriv

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

residuals-method

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

MixedClass-class

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

DiscreteClass-class

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