mirt1.42 package

Multidimensional Item Response Theory

fixedCalib

Fixed-item calibration method

fscores

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

M2

Compute the M2 model fit statistic

anova-method

Compare nested models with likelihood-based statistics

areainfo

Function to calculate the area under a selection of information curves

averageMI

Collapse values from multiple imputation draws

bfactor

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

boot.LR

Parametric bootstrap likelihood-ratio test

boot.mirt

Calculate bootstrapped standard errors for estimated models

coef-method

Extract raw coefs from model object

createGroup

Create a user defined group-level object with correct generic function...

createItem

Create a user defined item with correct generic functions

DIF

Differential item functioning statistics

DiscreteClass-class

Class "DiscreteClass"

draw_parameters

Draw plausible parameter instantiations from a given model

DRF

Differential Response Functioning statistics

DTF

Differential test functioning statistics

empirical_ES

Empirical effect sizes based on latent trait estimates

empirical_plot

Function to generate empirical unidimensional item and test plots

empirical_rxx

Function to calculate the empirical (marginal) reliability

estfun.AllModelClass

Extract Empirical Estimating Functions

fixef

Compute latent regression fixed effect expected values

expand.table

Expand summary table of patterns and frequencies

expected.item

Function to calculate expected value of item

expected.test

Function to calculate expected test score

extract.group

Extract a group from a multiple group mirt object

extract.item

Extract an item object from mirt objects

extract.mirt

Extract various elements from estimated model objects

gen.difficulty

Generalized item difficulty summaries

imputeMissing

Imputing plausible data for missing values

itemfit

Item fit statistics

itemGAM

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

iteminfo

Function to calculate item information

itemplot

Displays item surface and information plots

multipleGroup

Multiple Group Estimation

itemstats

Generic item summary statistics

key2binary

Score a test by converting response patterns to binary data

lagrange

Lagrange test for freeing parameters

likert2int

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

logLik-method

Extract log-likelihood

print.mirt_matrix

Print generic for customized matrix console output

marginal_rxx

Function to calculate the marginal reliability

MDIFF

Compute multidimensional difficulty index

mdirt

Multidimensional discrete item response theory

MDISC

Compute multidimensional discrimination index

mirt-package

Full information maximum likelihood estimation of IRT models.

mirt.model

Specify model information

wald

Wald statistics for mirt models

mirt

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

mirtCluster

Define a parallel cluster object to be used in internal functions

MixedClass-class

Class "MixedClass"

mixedmirt

Mixed effects modeling for MIRT models

MixtureClass-class

Class "MixtureClass"

mod2values

Convert an estimated mirt model to a data.frame

MultipleGroupClass-class

Class "MultipleGroupClass"

numerical_deriv

Compute numerical derivatives

personfit

Person fit statistics

PLCI.mirt

Compute profiled-likelihood (or posterior) confidence intervals

plot-method

Plot various test-implied functions from models

poly2dich

Change polytomous items to dichotomous item format

print-method

Print the model objects

print.mirt_df

Print generic for customized data.frame console output

print.mirt_list

Print generic for customized list console output

probtrace

Function to calculate probability trace lines

randef

Compute posterior estimates of random effect

RCI

Model-based Reliable Change Index

read.mirt

Translate mirt parameters into suitable structure for plink package

remap.distance

Remap item categories to have integer distances of 1

residuals-method

Compute model residuals

reverse.score

Reverse score one or more items from a response matrix

RMSD_DIF

RMSD effect size statistic to quantify category-level DIF

secondOrderTest

Second-order test of convergence

show-method

Show model object

SIBTEST

(Generalized) Simultaneous Item Bias Test (SIBTEST)

simdata

Simulate response patterns

SingleGroupClass-class

Class "SingleGroupClass"

summary-method

Summary of model object

testinfo

Function to calculate test information

thetaComb

Create all possible combinations of vector input

traditional2mirt

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

vcov-method

Extract parameter variance covariance matrix

Analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported.

  • Maintainer: Phil Chalmers
  • License: GPL (>= 3)
  • Last published: 2024-07-14