All functions

automatic.recode()

Automatic Method of Finding Keys in a Dataset with Raw Item Responses

brm.sim() brm.irf()

Functions for the Beta Item Response Model

btm() summary(<btm>) predict(<btm>) btm_sim()

Extended Bradley-Terry Model

categorize() decategorize()

Categorize and Decategorize Variables in a Data Frame

ccov.np()

Nonparametric Estimation of Conditional Covariances of Item Pairs

cfa_meas_inv()

Estimation of a Unidimensional Factor Model under Full and Partial Measurement Invariance

class.accuracy.rasch()

Classification Accuracy in the Rasch Model

conf.detect() summary(<conf.detect>)

Confirmatory DETECT and polyDETECT Analysis

data.activity.itempars

Item Parameters Cultural Activities

data.befki data.befki_resp

BEFKI Dataset (Schroeders, Schipolowski, & Wilhelm, 2015)

data.big5 data.big5.qgraph

Dataset Big 5 from qgraph Package

data.bs07a

Datasets from Borg and Staufenbiel (2007)

data.eid.kap4 data.eid.kap5 data.eid.kap6 data.eid.kap7

Examples with Datasets from Eid and Schmidt (2014)

data.ess2005

Dataset European Social Survey 2005

data.g308

C-Test Datasets

data.inv4gr

Dataset for Invariance Testing with 4 Groups

data.liking.science

Dataset 'Liking For Science'

data.long

Longitudinal Dataset

data.lsem01 data.lsem02 data.lsem03

Datasets for Local Structural Equation Models / Moderated Factor Analysis

data.math

Dataset Mathematics

data.mcdonald.act15 data.mcdonald.LSAT6 data.mcdonald.rape

Some Datasets from McDonald's Test Theory Book

data.mixed1

Dataset with Mixed Dichotomous and Polytomous Item Responses

data.ml1 data.ml2

Multilevel Datasets

data.noharmExC data.noharm18

Datasets for NOHARM Analysis

data.pars1.rasch data.pars1.2pl

Item Parameters for Three Studies Obtained by 1PL and 2PL Estimation

data.pirlsmissing

Dataset from PIRLS Study with Missing Responses

data.pisaMath

Dataset PISA Mathematics

data.pisaPars

Item Parameters from Two PISA Studies

data.pisaRead

Dataset PISA Reading

data.pw01

Datasets for Pairwise Comparisons

data.ratings1 data.ratings2 data.ratings3

Rating Datasets

data.raw1

Dataset with Raw Item Responses

data.read

Dataset Reading

data.reck21 data.reck61DAT1 data.reck61DAT2 data.reck73C1a data.reck73C1b data.reck75C2 data.reck78ExA data.reck79ExB

Datasets from Reckase' Book Multidimensional Item Response Theory

data.si01 data.si02 data.si03 data.si04 data.si05 data.si06 data.si07 data.si08 data.si09 data.si10

Some Example Datasets for the sirt Package

data.timss

Dataset TIMSS Mathematics

data.timss07.G8.RUS

TIMSS 2007 Grade 8 Mathematics and Science Russia

data.trees

Dataset Used in Stoyan, Pommerening and Wuensche (2018)

data.wide2long()

Converting a Data Frame from Wide Format in a Long Format

detect.index()

Calculation of the DETECT and polyDETECT Index

dif.logistic.regression()

Differential Item Functioning using Logistic Regression Analysis

dif.strata.variance()

Stratified DIF Variance

dif.variance()

DIF Variance

dirichlet.mle()

Maximum Likelihood Estimation of the Dirichlet Distribution

dirichlet.simul()

Simulation of a Dirichlet Distributed Vectors

eigenvalues.manymatrices()

Computation of Eigenvalues of Many Symmetric Matrices

equating.rasch.jackknife()

Jackknife Equating Error in Generalized Logistic Rasch Model

equating.rasch()

Equating in the Generalized Logistic Rasch Model

expl.detect()

Exploratory DETECT Analysis

f1d.irt()

Functional Unidimensional Item Response Model

fit.isop() fit.adisop()

Fitting the ISOP and ADISOP Model for Frequency Tables

fuzcluster() summary(<fuzcluster>)

Clustering for Continuous Fuzzy Data

fuzdiscr()

Estimation of a Discrete Distribution for Fuzzy Data (Data in Belief Function Framework)

gom.em() summary(<gom>) anova(<gom>) logLik(<gom>) IRT.irfprob(<gom>) IRT.likelihood(<gom>) IRT.posterior(<gom>) IRT.modelfit(<gom>) summary(<IRT.modelfit.gom>)

Discrete (Rasch) Grade of Membership Model

gom.jml()

Grade of Membership Model (Joint Maximum Likelihood Estimation)

greenyang.reliability()

Reliability for Dichotomous Item Response Data Using the Method of Green and Yang (2009)

invariance.alignment() summary(<invariance.alignment>) invariance_alignment_constraints() summary(<invariance_alignment_constraints>) invariance_alignment_simulate() invariance_alignment_cfa_config()

Alignment Procedure for Linking under Approximate Invariance

IRT.mle()

Person Parameter Estimation

isop.dich() isop.poly() summary(<isop>) plot(<isop>)

Fit Unidimensional ISOP and ADISOP Model to Dichotomous and Polytomous Item Responses

isop.scoring()

Scoring Persons and Items in the ISOP Model

isop.test() summary(<isop.test>)

Testing the ISOP Model

latent.regression.em.raschtype() latent.regression.em.normal() summary(<latent.regression>)

Latent Regression Model for the Generalized Logistic Item Response Model and the Linear Model for Normal Responses

lavaan2mirt()

Converting a lavaan Model into a mirt Model

lc.2raters() summary(<lc.2raters>)

Latent Class Model for Two Exchangeable Raters and One Item

likelihood.adjustment()

Adjustment and Approximation of Individual Likelihood Functions

linking.haberman() summary(<linking.haberman>) linking.haberman.lq() summary(<linking.haberman.lq>) linking_haberman_itempars_prepare() linking_haberman_itempars_convert() L0_polish()

Linking in the 2PL/Generalized Partial Credit Model

linking.haebara() summary(<linking.haebara>)

Haebara Linking of the 2PL Model for Multiple Studies

linking.robust() summary(<linking.robust>) plot(<linking.robust>)

Robust Linking of Item Intercepts

lq_fit() lq_fit_estimate_power() dexppow() rexppow()

Fit of a \(L_q\) Regression Model

lsdm() summary(<lsdm>) plot(<lsdm>)

Least Squares Distance Method of Cognitive Validation

lsem.estimate() summary(<lsem>) plot(<lsem>) lsem.MGM.stepfunctions() lsem_local_weights() lsem.bootstrap()

Local Structural Equation Models (LSEM)

lsem.permutationTest() summary(<lsem.permutationTest>) plot(<lsem.permutationTest>)

Permutation Test for a Local Structural Equation Model

lsem.test()

Test a Local Structural Equation Model Based on Bootstrap

marginal.truescore.reliability()

True-Score Reliability for Dichotomous Data

rowMaxs.sirt() rowMins.sirt() rowCumsums.sirt() colCumsums.sirt() rowIntervalIndex.sirt() rowKSmallest.sirt() rowKSmallest2.sirt()

Some Matrix Functions

mcmc.2pno.ml()

Random Item Response Model / Multilevel IRT Model

mcmc.2pno()

MCMC Estimation of the Two-Parameter Normal Ogive Item Response Model

mcmc.2pnoh()

MCMC Estimation of the Hierarchical IRT Model for Criterion-Referenced Measurement

mcmc.3pno.testlet()

3PNO Testlet Model

mcmc.list.descriptives()

Computation of Descriptive Statistics for a mcmc.list Object

mcmclist2coda()

Write Coda File from an Object of Class mcmc.list

mcmc_coef() mcmc_vcov() mcmc_confint() mcmc_summary() mcmc_plot() mcmc_derivedPars() mcmc_WaldTest() summary(<mcmc_WaldTest>)

Some Methods for Objects of Class mcmc.list

mcmc_Rhat()

Computation of the Rhat Statistic from a Single MCMC Chain

md.pattern.sirt()

Response Pattern in a Binary Matrix

mirt.specify.partable()

Specify or modify a Parameter Table in mirt

mirt.wrapper.coef() mirt_summary() mirt.wrapper.posterior() IRT.likelihood(<SingleGroupClass>) IRT.likelihood(<MultipleGroupClass>) IRT.posterior(<SingleGroupClass>) IRT.posterior(<MultipleGroupClass>) IRT.expectedCounts(<SingleGroupClass>) IRT.expectedCounts(<MultipleGroupClass>) IRT.irfprob(<SingleGroupClass>) IRT.irfprob(<MultipleGroupClass>) mirt.wrapper.fscores() mirt.wrapper.itemplot()

Some Functions for Wrapping with the mirt Package

mle.pcm.group()

Maximum Likelihood Estimation of Person or Group Parameters in the Generalized Partial Credit Model

modelfit.sirt() modelfit.cor.poly()

Assessing Model Fit and Local Dependence by Comparing Observed and Expected Item Pair Correlations

monoreg.rowwise() monoreg.colwise()

Monotone Regression for Rows or Columns in a Matrix

nedelsky.sim() nedelsky.latresp() nedelsky.irf()

Functions for the Nedelsky Model

noharm.sirt() summary(<noharm.sirt>)

NOHARM Model in R

np.dich()

Nonparametric Estimation of Item Response Functions

parmsummary_extend()

Includes Confidence Interval in Parameter Summary Table

pbivnorm2()

Cumulative Function for the Bivariate Normal Distribution

pcm.conversion()

Conversion of the Parameterization of the Partial Credit Model

pcm.fit()

Item and Person Fit Statistics for the Partial Credit Model

person.parameter.rasch.copula()

Person Parameter Estimation of the Rasch Copula Model (Braeken, 2011)

personfit.stat()

Person Fit Statistics for the Rasch Model

pgenlogis() genlogis.moments()

Calculation of Probabilities and Moments for the Generalized Logistic Item Response Model

plausible.value.imputation.raschtype()

Plausible Value Imputation in Generalized Logistic Item Response Model

plot(<mcmc.sirt>)

Plot Function for Objects of Class mcmc.sirt

plot(<np.dich>)

Plot Method for Object of Class np.dich

polychoric2() sirt_rcpp_polychoric2()

Polychoric Correlation

prior_model_parse()

Parsing a Prior Model

prmse.subscores.scales()

Proportional Reduction of Mean Squared Error (PRMSE) for Subscale Scores

prob.guttman() summary(<prob.guttman>) anova(<prob.guttman>) logLik(<prob.guttman>) IRT.irfprob(<prob.guttman>) IRT.likelihood(<prob.guttman>) IRT.posterior(<prob.guttman>)

Probabilistic Guttman Model

Q3()

Estimation of the \(Q_3\) Statistic (Yen, 1984)

Q3.testlet()

\(Q_3\) Statistic of Yen (1984) for Testlets

qmc.nodes()

Calculation of Quasi Monte Carlo Integration Points

R2conquest() summary(<R2conquest>) read.show() read.show.term() read.show.regression() read.pv() read.multidimpv() read.pimap()

Running ConQuest From Within R

R2noharm.EAP()

EAP Factor Score Estimation

R2noharm.jackknife() summary(<R2noharm.jackknife>)

Jackknife Estimation of NOHARM Analysis

R2noharm() summary(<R2noharm>)

Estimation of a NOHARM Analysis from within R

rasch.copula2() rasch.copula3() summary(<rasch.copula2>) summary(<rasch.copula3>) anova(<rasch.copula2>) anova(<rasch.copula3>) logLik(<rasch.copula2>) logLik(<rasch.copula3>) IRT.likelihood(<rasch.copula2>) IRT.likelihood(<rasch.copula3>) IRT.posterior(<rasch.copula2>) IRT.posterior(<rasch.copula3>)

Multidimensional IRT Copula Model

rasch.evm.pcm() summary(<rasch.evm.pcm>) coef(<rasch.evm.pcm>) vcov(<rasch.evm.pcm>)

Estimation of the Partial Credit Model using the Eigenvector Method

rasch.jml.biascorr()

Bias Correction of Item Parameters for Joint Maximum Likelihood Estimation in the Rasch model

rasch.jml.jackknife1()

Jackknifing the IRT Model Estimated by Joint Maximum Likelihood (JML)

rasch.jml() summary(<rasch.jml>)

Joint Maximum Likelihood (JML) Estimation of the Rasch Model

rasch.mirtlc() summary(<rasch.mirtlc>) anova(<rasch.mirtlc>) logLik(<rasch.mirtlc>) IRT.irfprob(<rasch.mirtlc>) IRT.likelihood(<rasch.mirtlc>) IRT.posterior(<rasch.mirtlc>) IRT.modelfit(<rasch.mirtlc>) summary(<IRT.modelfit.rasch.mirtlc>)

Multidimensional Latent Class 1PL and 2PL Model

rasch.mml2() summary(<rasch.mml>) plot(<rasch.mml>) anova(<rasch.mml>) logLik(<rasch.mml>) IRT.irfprob(<rasch.mml>) IRT.likelihood(<rasch.mml>) IRT.posterior(<rasch.mml>) IRT.modelfit(<rasch.mml>) IRT.expectedCounts(<rasch.mml>) summary(<IRT.modelfit.rasch.mml>)

Estimation of the Generalized Logistic Item Response Model, Ramsay's Quotient Model, Nonparametric Item Response Model, Pseudo-Likelihood Estimation and a Missing Data Item Response Model

rasch.pairwise.itemcluster()

Pairwise Estimation of the Rasch Model for Locally Dependent Items

rasch.pairwise() summary(<rasch.pairwise>)

Pairwise Estimation Method of the Rasch Model

rasch.pml3() summary(<rasch.pml>)

Pairwise Marginal Likelihood Estimation for the Probit Rasch Model

rasch.prox()

PROX Estimation Method for the Rasch Model

rasch.va()

Estimation of the Rasch Model with Variational Approximation

reliability.nonlinearSEM()

Estimation of Reliability for Confirmatory Factor Analyses Based on Dichotomous Data

resp_groupwise()

Creates Group-Wise Item Response Dataset

rinvgamma2() dinvgamma2()

Inverse Gamma Distribution in Prior Sample Size Parameterization

rm.facets() summary(<rm.facets>) anova(<rm.facets>) logLik(<rm.facets>) IRT.irfprob(<rm.facets>) IRT.factor.scores(<rm.facets>) IRT.likelihood(<rm.facets>) IRT.posterior(<rm.facets>) IRT.modelfit(<rm.facets>) summary(<IRT.modelfit.rm.facets>) rm_proc_data()

Rater Facets Models with Item/Rater Intercepts and Slopes

rm.sdt() summary(<rm.sdt>) plot(<rm.sdt>) anova(<rm.sdt>) logLik(<rm.sdt>) IRT.factor.scores(<rm.sdt>) IRT.irfprob(<rm.sdt>) IRT.likelihood(<rm.sdt>) IRT.posterior(<rm.sdt>) IRT.modelfit(<rm.sdt>) summary(<IRT.modelfit.rm.sdt>)

Hierarchical Rater Model Based on Signal Detection Theory (HRM-SDT)

rmvn() ruvn()

Simulation of a Multivariate Normal Distribution with Exact Moments

scale_group_means() predict_scale_group_means()

Scaling of Group Means and Standard Deviations

sia.sirt()

Statistical Implicative Analysis (SIA)

sim.qm.ramsay()

Simulate from Ramsay's Quotient Model

sim.rasch.dep()

Simulation of the Rasch Model with Locally Dependent Responses

sim.raschtype()

Simulate from Generalized Logistic Item Response Model

rasch.conquest() rasch.pml2() testlet.yen.q3() yen.q3()

Defunct sirt Functions

sirt-package

Supplementary Item Response Theory Models

bounds_parameters() dimproper() ginverse_sym() hard_thresholding() soft_thresholding() pow() tracemat() sirt_matrix2() sirt_colMeans() sirt_colSDs() sirt_colMins() sirt_colMaxs() sirt_colMedians() sirt_sum_norm() sirt_dnorm_discrete() sirt_rbind_fill() sirt_fisherz() sirt_antifisherz() sirt_abs_smooth() sirt_permutations() sirt_attach_list_elements() sirt_optimizer() sirt_summary_print_objects() sirt_summary_print_package_rsession() sirt_summary_print_package() sirt_summary_print_rsession() sirt_summary_print_call() print_digits() sirt_rcpp_discrete_inverse() move_variables_df()

Utility Functions in sirt

sirt_eigenvalues()

First Eigenvalues of a Symmetric Matrix

smirt() summary(<smirt>) anova(<smirt>) logLik(<smirt>) IRT.irfprob(<smirt>) IRT.likelihood(<smirt>) IRT.posterior(<smirt>) IRT.modelfit(<smirt>) summary(<IRT.modelfit.smirt>)

Multidimensional Noncompensatory, Compensatory and Partially Compensatory Item Response Model

stratified.cronbach.alpha()

Stratified Cronbach's Alpha

summary(<mcmc.sirt>)

Summary Method for Objects of Class mcmc.sirt

tam2mirt()

Converting a fitted TAM Object into a mirt Object

testlet.marginalized()

Marginal Item Parameters from a Testlet (Bifactor) Model

tetrachoric2()

Tetrachoric Correlation Matrix

truescore.irt()

Conversion of Trait Scores \(\theta\) into True Scores \(\tau ( \theta )\)

unidim.test.csn()

Test for Unidimensionality of CSN

wle.rasch.jackknife()

Standard Error Estimation of WLE by Jackknifing

wle.rasch()

Weighted Likelihood Estimation of Person Abilities

xxirt() summary(<xxirt>) print(<xxirt>) anova(<xxirt>) coef(<xxirt>) logLik(<xxirt>) vcov(<xxirt>) confint(<xxirt>) IRT.expectedCounts(<xxirt>) IRT.factor.scores(<xxirt>) IRT.irfprob(<xxirt>) IRT.likelihood(<xxirt>) IRT.posterior(<xxirt>) IRT.modelfit(<xxirt>) summary(<IRT.modelfit.xxirt>) IRT.se(<xxirt>) xxirt_hessian()

User Defined Item Response Model

xxirt_createDiscItem() xxirt_createParTable() xxirt_modifyParTable()

Create Item Response Functions and Item Parameter Table

xxirt_createThetaDistribution()

Creates a User Defined Theta Distribution