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MLCIRTwithin (version 1.0)

Latent Class Item Response Theory Models Under 'Within-Item Multi-Dimensionality'

Description

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.

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Version

Install

install.packages('MLCIRTwithin')

Monthly Downloads

217

Version

1.0

License

GPL (>= 2)

Maintainer

Francesco Bartolucci

Last Published

June 3rd, 2015

Functions in MLCIRTwithin (1.0)

RLMS

RLMS dataset
summary.est_multi_poly_within

Print the output of test_dim object
est_multi_poly_within

Estimate LC IRT model for dichotomous and polytomous responses under within multidimensionality
lk_obs_score_within

Compute observed log-likelihood and score
est_multi_glob_gen

Fit marginal regression models for categorical responses
prob_multi_glob_gen

Global probabilities
MLCIRTwithin-package

Latent Class (LC) Item Response Theory (IRT) Models Under 'Within-Item Multi-Dimensionality'
print.est_multi_poly_within

Print the output of est_multi_poly_within object