mixtools

R package mixtools: Tools for analyzing finite mixture models. A collection of R functions for analyzing finite mixture models. This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772.

Keywords for this software

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References in zbMATH (referenced in 37 articles , 1 standard article )

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  1. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018)
  2. Angelo Mazza; Antonio Punzo; Salvatore Ingrassia: flexCWM: A Flexible Framework for Cluster-Weighted Models (2018)
  3. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  4. Ghosh, I.; Hamedani, G. G.; Bansal, N.; Maadooliat, M.: On the mixtures of Weibull and Pareto (IV) distribution: an alternative to Pareto distribution (2018)
  5. Jose Ameijeiras-Alonso, Rosa M. Crujeiras, Alberto Rodríguez-Casal: Multimode: An R Package for Mode Assessment (2018) arXiv
  6. Källberg, David; Belyaev, Yuri; Rydén, Patrik: A moment-distance hybrid method for estimating a mixture of two symmetric densities (2018)
  7. Michel Meulders; Philippe De Bruecker: Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data (2018)
  8. Miroshnichenko, Vitalii; Maiboroda, Rostyslav: Confidence ellipsoids for regression coefficients by observations from a mixture (2018)
  9. 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)
  10. Hu, Hao; Yao, Weixin; Wu, Yichao: The robust EM-type algorithms for log-concave mixtures of regression models (2017)
  11. Saptarshi Chakraborty, Samuel W.K. Wong: BAMBI: An R package for Fitting Bivariate Angular Mixture Models (2017) arXiv
  12. Bordes, Laurent; Chauveau, Didier: Stochastic EM algorithms for parametric and semiparametric mixture models for right-censored lifetime data (2016)
  13. Chauveau, Didier; Hoang, Vy Thuy Lynh: Nonparametric mixture models with conditionally independent multivariate component densities (2016)
  14. Dalla Valle, Luciana; De Giuli, Maria Elena; Tarantola, Claudia; Manelli, Claudio: Default probability estimation via pair copula constructions (2016)
  15. Hu, Hao; Wu, Yichao; Yao, Weixin: Maximum likelihood estimation of the mixture of log-concave densities (2016)
  16. Li, Meng; Xiang, Sijia; Yao, Weixin: Robust estimation of the number of components for mixtures of linear regression models (2016)
  17. Nguyen, Hien D.; McLachlan, Geoffrey J.: Linear mixed models with marginally symmetric nonparametric random effects (2016)
  18. Shepherd, Bryan E.; Li, Chun; Liu, Qi: Probability-scale residuals for continuous, discrete, and censored data (2016)
  19. Cecile Proust-Lima, Viviane Philipps, Benoit Liquet: Estimation of extended mixed models using latent classes and latent processes: the R package lcmm (2015) arXiv
  20. Chauveau, Didier; Hunter, David R.; Levine, Michael: Semi-parametric estimation for conditional independence multivariate finite mixture models (2015)

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