Analysis of dichotomous and polytomous response data using
latent trait models under the Item Response Theory paradigm.
Includes univariate and multivariate one-, two-, three-, and
four-parameter logistic models, graded response models,
generalized partial credit models, nominal models, multiple
choice models, and multivariate partially-compensatory models.
These can be used in an exploratory or confirmatory manner with
optional user defined linear constraints. 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. Additionally, multiple group
analysis may be performed for unidimensional or
multidimensional item response models for detecting
differential item functioning.
stats4, psych, GPArotation, mvtnorm, Rcpp, numDeriv, Matrix
'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' 'MHRM.R' '01-itemtypes.R'
'02-item_methods.R' 'calcLogLik.R' 'EMstep.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'

Analysis of dichotomous and polytomous response data using
latent trait models under the Item Response Theory paradigm.
Includes univariate and multivariate one-, two-, three-, and
four-parameter logistic models, graded response models, rating
scale graded response models, generalized partial credit
models, nominal models, multiple choice models, and
multivariate partially-compensatory models. These can be used
in an exploratory or confirmatory manner with optional user
defined linear constraints. 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. Additionally, multiple group analysis may be
performed for unidimensional or multidimensional item response
models for detecting differential item functioning.
'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'