R package mirt: Multidimensional Item Response Theory. Analysis of dichotomous and polytomous response data using unidimensional and multidimensional latent trait models under the Item Response Theory paradigm. Exploratory and confirmatory models can be estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier analyses are available for modeling item testlets. Multiple group analysis and mixed effects designs also are available for detecting differential item functioning and modelling item and person covariates.

References in zbMATH (referenced in 22 articles , 1 standard article )

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  1. Chen, Yunxiao; Li, Xiaoou; Zhang, Siliang: Joint maximum likelihood estimation for high-dimensional exploratory item factor analysis (2019)
  2. Jiang, Zhehan; Templin, Jonathan: Gibbs samplers for logistic item response models via the Pólya-gamma distribution: a computationally efficient data-augmentation strategy (2019)
  3. Jin, Ick Hoon; Jeon, Minjeong: A doubly latent space joint model for local item and person dependence in the analysis of item response data (2019)
  4. Martínez-Plumed, Fernando; Prudêncio, Ricardo B. C.; Martínez-Usó, Adolfo; Hernández-Orallo, José: Item response theory in AI: analysing machine learning classifiers at the instance level (2019)
  5. Chalmers, R. Philip: Model-based measures for detecting and quantifying response bias (2018)
  6. Chalmers, R. Philip: Improving the crossing-SIBTEST statistic for detecting non-uniform DIF (2018)
  7. Fontanella, Lara; Sarra, Annalina; Valentini, Pasquale; Di Zio, Simone; Fontanella, Sara: Varying levels of anomie in Europe: a multilevel analysis based on multidimensional IRT models (2018)
  8. Liu, Yang; Yang, Ji Seung: Bootstrap-calibrated interval estimates for latent variable scores in item response theory (2018)
  9. Mair, Patrick: Modern psychometrics with R (2018)
  10. Wang, Ting; Strobl, Carolin; Zeileis, Achim; Merkle, Edgar C.: Score-based tests of differential item functioning via pairwise maximum likelihood estimation (2018)
  11. Andersson, Björn; Wiberg, Marie: Item response theory observed-score kernel equating (2017)
  12. Bartolucci, Francesco; Farcomeni, Alessio; Scaccia, Luisa: A nonparametric multidimensional latent class IRT model in a Bayesian framework (2017)
  13. Hernández-Sánchez, Julio César; Vicente-Villardón, José Luis: Logistic biplot for nominal data (2017)
  14. Liu, Yang; Hannig, Jan: Generalized fiducial inference for logistic graded response models (2017)
  15. Víctor Cervantes: DFIT: An R Package for Raju’s Differential Functioning of Items and Tests Framework (2017) not zbMATH
  16. Jorge Tendeiro and Rob Meijer and A. Niessen: PerFit: An R Package for Person-Fit Analysis in IRT (2016) not zbMATH
  17. van der Linden, Wim J.; Barrett, Michelle D.: Linking item response model parameters (2016)
  18. Huo, Yan; de la Torre, Jimmy; Mun, Eun-Young; Kim, Su-Young; Ray, Anne E.; Jiao, Yang; White, Helene R.: A hierarchical multi-unidimensional IRT approach for analyzing sparse, multi-group data for integrative data analysis (2015)
  19. Michela Battauz: equateIRT: An R Package for IRT Test Equating (2015) not zbMATH
  20. Wang, Chun: On latent trait estimation in multidimensional compensatory item response models (2015)

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