MultiLCIRT

R package MultiLCIRT: Multidimensional Latent Class Item Response Theory Models. 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).


References in zbMATH (referenced in 12 articles , 2 standard articles )

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  1. Espinosa, Javier; Hennig, Christian: A constrained regression model for an ordinal response with ordinal predictors (2019)
  2. Padilla, Juan L.; Azevedo, Caio L. N.; Lachos, Victor H.: Multidimensional multiple group IRT models with skew normal latent trait distributions (2018)
  3. Bartolucci, Francesco; Farcomeni, Alessio; Scaccia, Luisa: A nonparametric multidimensional latent class IRT model in a Bayesian framework (2017)
  4. Bartolucci, Francesco; Bacci, Silvia; Gnaldi, Michela: Statistical analysis of questionnaires. A unified approach based on R and Stata (2016)
  5. Bartolucci, Francesco; Montanari, Giorgio E.; Pandolfi, Silvia: Item selection by latent class-based methods: an application to nursing home evaluation (2016)
  6. Bustamante, Juan Carlos; Chacón, Edixon: Estimation and goodness-of-fit in latent trait models: a comparison among theoretical approaches (2016)
  7. Dal Bianco, Chiara; Paccagnella, Omar; Varriale, Roberta: A multilevel latent class analysis of the purchasing channels among European consumers (2016)
  8. Gnaldi, Michela; Bacci, Silvia; Bartolucci, Francesco: A multilevel finite mixture item response model to cluster examinees and schools (2016)
  9. Brusco, Michael J.; Köhn, Hans-Friedrich; Steinley, Douglas: An exact method for partitioning dichotomous items within the framework of the monotone homogeneity model (2015)
  10. Bacci, Silvia; Bartolucci, Francesco; Gnaldi, Michela: A class of multidimensional latent class IRT models for ordinal polytomous item responses (2014)
  11. Bartolucci, Francesco; Bacci, Silvia; Gnaldi, Michela: MultiLCIRT: an R package for multidimensional latent class item response models (2014)
  12. Filzmoser, Peter (ed.); Gatu, Cristian (ed.); Zeileis, Achim (ed.): Special issue on statistical algorithms and software in R (2014)