polymirt

arguments to be passed to the
  <code><a href="/link/confmirt?package=mirt&version=0.6.0" rd-options="" data-mini-rdoc="mirt::confmirt">confmirt</a></code> estimation engine

<code>polymirt</code> fits an unconditional (exploratory)
  full-information maximum-likelihood factor analysis model
  to dichotomous and polytomous data under the item
  response theory paradigm using Cai's (2010)
  Metropolis-Hastings Robbins-Monro algorithm. The function
  is currently depreciated and instead should be run by
  using the <code><a href="/link/confmirt?package=mirt&version=0.6.0" rd-options="" data-mini-rdoc="mirt::confmirt">confmirt</a></code> function.

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.

polymirt: Full-Information Item Factor Analysis for Mixed Data Formats

Description

Usage

Arguments

See Also