This package provides a flexible framework for the Item
Response Theory analysis of dichotomous and ordinal polythomous
outcomes under the assumption of multidimensionality 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 (2.0).

This package provides a flexible framework for the Item
Response Theory analysis of dichotomous and ordinal polythomous
outcomes under the assumption of multidimensionality 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 (2.1).

Multidimensional Latent Class Item Response Theory Models
Francesco Bartolucci, Silvia Bacci, Michela Gnaldi - University of Perugia (IT)
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality 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 (since 2.1).

This package provides a flexible framework for the Item
Response Theory analysis of dichotomous and ordinal polytomous
outcomes under the assumption of multidimensionality 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 (since 2.1).

This package provides a flexible framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality 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 (since 2.1).