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 52 articles , 1 standard article )

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  1. Dychko, H. M.; Maĭboroda, R. E.: A generalized Nadaraya-Watson estimator for observations obtained from a mixture (2020)
  2. Akakpo, Rexford M.; Xia, Michelle; Polansky, Alan M.: Frequentist inference in insurance ratemaking models adjusting for misrepresentation (2019)
  3. Blair R. Drummond, Christian J.G. Tessier, Mathieu F. Dextraze, Corrie J.B. daCosta: scbursts: An R package for analysis and sorting of single-channel bursts (2019) not zbMATH
  4. Geissen, Eva-Maria; Hasenauer, Jan; Radde, Nicole E.: Inference of finite mixture models and the effect of binning (2019)
  5. Gualandi, Stefano; Toscani, Giuseppe: Human behavior and lognormal distribution. A kinetic description (2019)
  6. Lim, Johan; Yu, Donghyeon; Kuo, Hsun-Chih; Choi, Hyungwon; Walmsley, Scott: Truncated rank correlation (TRC) as a robust measure of test-retest reliability in mass spectrometry data (2019)
  7. Liu, Yiyi; Warren, Joshua L.; Zhao, Hongyu: A hierarchical Bayesian model for single-cell clustering using RNA-sequencing data (2019)
  8. Maĭboroda, R. E.; Navara, G. V.; Sugakova, O. V.: Orthogonal regression method for observations from a mixture (2019)
  9. Michael Hahsler; Matthew Piekenbrock; Derek Doran: dbscan: Fast Density-Based Clustering with R (2019) not zbMATH
  10. Nguyen, Hien; Yee, Yohan; McLachlan, Geoffrey; Lerch, Jason: False discovery rate control for grouped or discretely supported (p)-values with application to a neuroimaging study (2019)
  11. Young, Derek S.; Chen, Xi; Hewage, Dilrukshi C.; Nilo-Poyanco, Ricardo: Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering (2019)
  12. Zagaris, Antonios: Data-informed modeling in the health sciences (2019)
  13. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  14. Angelo Mazza; Antonio Punzo; Salvatore Ingrassia: flexCWM: A Flexible Framework for Cluster-Weighted Models (2018) not zbMATH
  15. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  16. Ghosh, I.; Hamedani, G. G.; Bansal, N.; Maadooliat, M.: On the mixtures of Weibull and Pareto (IV) distribution: an alternative to Pareto distribution (2018)
  17. Jose Ameijeiras-Alonso, Rosa M. Crujeiras, Alberto Rodríguez-Casal: Multimode: An R Package for Mode Assessment (2018) arXiv
  18. Källberg, David; Belyaev, Yuri; Rydén, Patrik: A moment-distance hybrid method for estimating a mixture of two symmetric densities (2018)
  19. Michel Meulders; Philippe De Bruecker: Latent Class Probabilistic Latent Feature Analysis of Three-Way Three-Mode Binary Data (2018) not zbMATH
  20. Miroshnichenko, Vitalii; Maiboroda, Rostyslav: Confidence ellipsoids for regression coefficients by observations from a mixture (2018)

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