R package ordinal: Regression Models for Ordinal Data. Implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered logit/probit/... models. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature. Multiple random effect terms are allowed and they may be nested, crossed or partially nested/crossed. Restrictions of symmetry and equidistance can be imposed on the thresholds (cut-points/intercepts). Standard model methods are available (summary, anova, drop-methods, step, confint, predict etc.) in addition to profile methods and slice methods for visualizing the likelihood function and checking convergence.

References in zbMATH (referenced in 24 articles , 1 standard article )

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  1. Achim Zeileis, Susanne Köll, Nathaniel Graham: Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R (2020) not zbMATH
  2. Barahona, Sonia; Centella, Pablo; Gual-Arnau, Ximo; Ibáñez, M. Victoria; Simó, Amelia: Generalized linear models for geometrical current predictors: an application to predict garment fit (2020)
  3. Bu, Xianwei; Majumdar, Dibyen; Yang, Jie: D-optimal designs for multinomial logistic models (2020)
  4. Kristensen, Simon Bang; Sandberg, Kristian; Bibby, Bo Martin: Regression methods for metacognitive sensitivity (2020)
  5. Maurizio Manuguerra, Gillian Z. Heller, Jun Ma: Continuous Ordinal Regression for Analysis of Visual Analogue Scales: The R Package ordinalCont (2020) not zbMATH
  6. Rainer Hirk, Kurt Hornik, Laura Vana: mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020) not zbMATH
  7. Torsten Hothorn: Most Likely Transformations: The mlt Package (2020) not zbMATH
  8. Tutz, Gerhard: Modelling heterogeneity: on the problem of group comparisons with logistic regression and the potential of the heterogeneous choice model (2020)
  9. Haag, Fridolin; Zürcher, Sara; Lienert, Judit: Enhancing the elicitation of diverse decision objectives for public planning (2019)
  10. Jorge Cimentada: perccalc: An R package for estimating percentiles from categorical variables (2019) not zbMATH
  11. M. Cristina Heredia-Gómez; Salvador García; Pedro Antonio Gutiérrez; Francisco Herrera: OCAPIS: R package for Ordinal Classification And Preprocessing In Scala (2018) arXiv
  12. Agresti, Alan; Kateri, Maria: Ordinal probability effect measures for group comparisons in multinomial cumulative link models (2017)
  13. De Lara, I. A. R.; Hinde, J. P.; De Castro, A. C.; Da Silva, I. J. O.: A proportional odds transition model for ordinal responses with an application to pig behaviour (2017)
  14. De Lara, Idemauro Antonio Rodrigues; Hinde, John; Taconeli, Cesar Augusto: An alternative method for evaluating stationarity in transition models (2017)
  15. Paul-Christian Bürkner: brms: An R Package for Bayesian Multilevel Models Using Stan (2017) not zbMATH
  16. Ekstrøm, Claus Thorn: The R primer (2016)
  17. Irvine, Kathryn M.; Rodhouse, T. J.; Keren, Ilai N.: Extending ordinal regression with a latent zero-augmented beta distribution (2016)
  18. Tutz, Gerhard; Schmid, Matthias: Modeling discrete time-to-event data (2016)
  19. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv
  20. Hou, Jiayi; Archer, Kellie J.: Regularization method for predicting an ordinal response using longitudinal high-dimensional genomic data (2015)

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