Emmixuskew

R package EMMIXuskew: Fitting Unrestricted Multivariate Skew t Mixture Models. Functions to fit finite mixture of unrestricted multivariate skew t (FM-uMST) model, random sample generation, discriminant analysis, 2D and 3D contour plots

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References in zbMATH (referenced in 21 articles , 1 standard article )

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  1. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  2. Biernacki, Christophe; Lourme, Alexandre: Unifying data units and models in (co-)clustering (2019)
  3. Chacón, José E.: Mixture model modal clustering (2019)
  4. O’Hagan, Adrian; Murphy, Thomas Brendan; Scrucca, Luca; Gormley, Isobel Claire: Investigation of parameter uncertainty in clustering using a Gaussian mixture model via jackknife, bootstrap and weighted likelihood bootstrap (2019)
  5. Palczewski, Andrzej; Palczewski, Jan: Black-Litterman model for continuous distributions (2019)
  6. Tortora, Cristina; Franczak, Brian C.; Browne, Ryan P.; McNicholas, Paul D.: A mixture of coalesced generalized hyperbolic distributions (2019)
  7. Jeffrey Andrews; Jaymeson Wickins; Nicholas Boers; Paul McNicholas: teigen: An R Package for Model-Based Clustering and Classification via the Multivariate t Distribution (2018) not zbMATH
  8. Parisi, Antonio; Liseo, B.: Objective Bayesian analysis for the multivariate skew-(t) model (2018)
  9. Wallace, Meredith L.; Buysse, Daniel J.; Germain, Anne; Hall, Martica H.; Iyengar, Satish: Variable selection for skewed model-based clustering: application to the identification of novel sleep phenotypes (2018)
  10. Zhu, Xuwen; Melnykov, Volodymyr: Manly transformation in finite mixture modeling (2018)
  11. Brunero Liseo, Antonio Parisi: Objective Bayesian analysis for the multivariate skew-t model (2017) arXiv
  12. Clarke, Brenton R.; Davidson, Thomas; Hammarstrand, Robert: A comparison of the (L_2) minimum distance estimator and the EM-algorithm when fitting (k)-component univariate normal mixtures (2017)
  13. Jamalizadeh, Ahad; Lin, Tsung-I: A general class of scale-shape mixtures of skew-normal distributions: properties and estimation (2017)
  14. Murray, Paula M.; Browne, Ryan P.; McNicholas, Paul D.: Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering (2017)
  15. Azzalini, Adelchi; Browne, Ryan P.; Genton, Marc G.; McNicholas, Paul D.: On nomenclature for, and the relative merits of, two formulations of skew distributions (2016)
  16. Lin, Tsung-I; McLachlan, Geoffrey J.; Lee, Sharon X.: Extending mixtures of factor models using the restricted multivariate skew-normal distribution (2016)
  17. McLachlan, Geoffrey J.; Lee, Sharon X.: Comment on “On nomenclature, and the relative merits of two formulations of skew distributions” by A. Azzalini, R. Browne, M. Genton, and P. McNicholas (2016)
  18. Tortora, Cristina; McNicholas, Paul D.; Browne, Ryan P.: A mixture of generalized hyperbolic factor analyzers (2016)
  19. Sharon X. Lee, Geoffrey J. McLachlan: EMMIXcskew: an R Package for the Fitting of a Mixture of Canonical Fundamental Skew t-Distributions (2015) arXiv
  20. Wraith, Darren; Forbes, Florence: Location and scale mixtures of Gaussians with flexible tail behaviour: properties, inference and application to multivariate clustering (2015)

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