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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
dmlavaan()
Comparing Regression Parameters of Different lavaan Models Fitted to the
Same Dataset
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
locpolycor()
Local Modeling of Thresholds and Polychoric Correlations
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
mgsem()
Estimation of Multiple-Group Structural Equation Models
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()
IRT.modelfit(<sirt> )
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
sirt
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()
xxirt_sandwich_pml()
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