'bfactor.R' 'expand.table.R' 'itemplot.R' 'key2binary.R'
'fscores.R' 'mirt-package.R' 'confmirt.R' 'mirt.R' 'polymirt.R'
'simdata.R' '00-classes.R' 'utils.R' '01-itemtypes.R'
'02-item_methods.R' 'calcLogLik.R' 'LoadPars.R'
'confmirt.model.R' 'model.elements.R' 'PrepData.R'
'Confirmatory-methods.R' 'Exploratory-methods.R' 'MHRM.group.R'
'MultipleGroup-methods.R' 'multipleGroup.R' 'wald.R'
'EMstep.group.R' 'extract.item.R' 'iteminfo.R' 'itemfit.R'
'personfit.R' '03-estimation.R' '04-derivatives.R'
'read.mirt.R' 'fscores.internal.R' 'itemplot.internal.R'

Analysis of dichotomous and polytomous response data using
latent trait models under the Item Response Theory paradigm.
Exploratory models can be estimated via quadrature or
stochastic methods, a generalized confirmatory bi-factor
analysis is included, and confirmatory models can be fit with a
Metropolis-Hastings Robbins-Monro algorithm which can include
polynomial or product constructed latent traits. Multiple group
analysis and mixed effects designs may be performed for
unidimensional or multidimensional item response models for
detecting differential item functioning and modelling item and
person covariates.
'bfactor.R' 'expand.table.R' 'itemplot.R' 'key2binary.R'
'fscores.R' 'mirt-package.R' 'confmirt.R' 'mirt.R' 'polymirt.R'
'simdata.R' '00-classes.R' 'utils.R' '01-itemtypes.R'
'02-item_methods.R' 'calcLogLik.R' 'LoadPars.R'
'confmirt.model.R' 'model.elements.R' 'PrepData.R'
'Confirmatory-methods.R' 'Exploratory-methods.R' 'MHRM.group.R'
'MultipleGroup-methods.R' 'multipleGroup.R' 'wald.R'
'EMstep.group.R' 'extract.item.R' 'iteminfo.R' 'itemfit.R'
'personfit.R' '03-estimation.R' '04-derivatives.R'
'read.mirt.R' 'fscores.internal.R' 'itemplot.internal.R'
'mixedmirt.R' 'MHRM.mixed.R' 'Mixed-methods.R' 'fitIndices.R'
'testinfo.R'

Analysis of dichotomous and polytomous response data using
latent trait models under the Item Response Theory paradigm.
Exploratory models can be estimated via quadrature or
stochastic methods, a generalized confirmatory bi-factor
analysis is included, and confirmatory models can be fit with a
Metropolis-Hastings Robbins-Monro algorithm which may include
polynomial or product constructed latent traits. Multiple group
analysis and mixed effects designs may be performed for
unidimensional or multidimensional item response models for
detecting differential item functioning and modelling item and
person covariates.
stats4, GPArotation, mvtnorm, Rcpp, numDeriv
'bfactor.R' 'expand.table.R' 'itemplot.R' 'key2binary.R'
'fscores.R' 'mirt-package.R' 'confmirt.R' 'mirt.R' 'polymirt.R'
'simdata.R' '00-classes.R' 'utils.R' '01-itemtypes.R'
'02-item_methods.R' 'calcLogLik.R' 'LoadPars.R'
'confmirt.model.R' 'model.elements.R' 'PrepData.R'
'Confirmatory-methods.R' 'Exploratory-methods.R' 'MHRM.group.R'
'MultipleGroup-methods.R' 'multipleGroup.R' 'wald.R'
'EMstep.group.R' 'extract.item.R' 'iteminfo.R' 'itemfit.R'
'personfit.R' '03-estimation.R' '04-derivatives.R'
'read.mirt.R' 'fscores.internal.R' 'itemplot.internal.R'
'mixedmirt.R' 'MHRM.mixed.R' 'Mixed-methods.R' 'fitIndices.R'
'testinfo.R' 'imputeMissing.R' 'mod2values.R' 'probtrace.R'
'boot.mirt.R'