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
References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- Bordes, Laurent; Chauveau, Didier: Stochastic EM algorithms for parametric and semiparametric mixture models for right-censored lifetime data (2016)
- Li, Meng; Xiang, Sijia; Yao, Weixin: Robust estimation of the number of components for mixtures of linear regression models (2016)
- Owen, William Jason; Tony Ng, Hon Keung: Revisit of relationships and models for the Birnbaum-Saunders and inverse-Gaussian distributions (2015)
- van der Ven, Sanne H. G.; Straatemeier, Marthe; Jansen, Brenda R. J.; Klinkenberg, Sharon; van der Maas, Han L. J.: Learning multiplication: an integrated analysis of the multiplication ability of primary school children and the difficulty of single digit and multidigit multiplication problems (2015)
- Young, Derek S.: Mixtures of regressions with changepoints (2014)
- Bordes, L.; Kojadinovic, I.; Vandekerkhove, P.: Semiparametric estimation of a two-component mixture of linear regressions in which one component is known (2013)
- Chee, Chew-Seng; Wang, Yong: Estimation of finite mixtures with symmetric components (2013)
- Komárek, Arnošt; Komárková, Lenka: Clustering for multivariate continuous and discrete longitudinal data (2013)
- Vu, Duy Q.; Hunter, David R.; Schweinberger, Michael: Model-based clustering of large networks (2013)
- Schellhase, Christian; Kauermann, Göran: Density estimation and comparison with a penalized mixture approach (2012)
- Levine, M.; Hunter, D.R.; Chauveau, D.: Maximum smoothed likelihood for multivariate mixtures (2011)