Latent Class Item Response Theory Models Under 'Within-Item
Multi-Dimensionality'
Francesco Bartolucci, Silvia Bacci - University of Perugia (IT)
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multi-dimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version.
R (>= 2.0.0), MASS, limSolve, MultiLCIRT

Latent Class Item Response Theory (LC-IRT) Models under
Within-Item Multidimensionality
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parametrizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version.

Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parametrizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version together with possibility of constraints on all model parameters.

Kenneth Knoblauch and Laurence T. Maloney
R (>= 2.9), graphics, stats, utils, base
Conjoint measurement is a psychophysical procedure in
which stimulus pairs are presented that vary along n > 2
dimensions and the observer is required to compare the stimuli
along one of them.  This package contains functions to estimate
the contribution of the n scales to the judgment by a maximum
likelihood method under several hypotheses of how the
perceptual dimensions interact.