constructed latent traits. Additionally, multiple group
analysis may be performed for unidimensional or
multidimensional item response models for detecting
Imports: stats4, psych, GPArotation, mvtnorm, Rcpp, numDeriv, Matrix
'simdata.R' '00-classes.R' 'utils.R' 'MHRM.R' '01-itemtypes.R'

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.

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

Metropolis-Hastings Robbins-Monro algorithm which may include
Depends: R (>= 2.15), stats4, lattice, methods
Imports: stats4, GPArotation, mvtnorm, Rcpp, numDeriv

Suggests: testthat, plink, psych, boot, parallel
'fscores.R' 'mirt-package.R' 'confmirt.R' 'mirt.R' 'simdata.R'

Authors@R: c( person("Joshua", "Pritikin", role=c('ctb')), person("Phil",
"Chalmers", email = "rphilip.chalmers@gmail.com", role = c("aut", "cre")))
Description: 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.
Imports: GPArotation, mvtnorm, Rcpp, numDeriv
Suggests: testthat, plink, boot, latticeExtra, parallel