Robust Mixture Regression

Finite mixture models are a popular technique for modelling unobserved heterogeneity or to approximate general distribution functions in a semi-parametric way. They are used in a lot of different areas such as astronomy, biology, economics, marketing or medicine. This package is the implementation of popular robust mixture regression methods based on different algorithms including: fleximix, finite mixture models and latent class regression; CTLERob, component-wise adaptive trimming likelihood estimation; mixbi, bi-square estimation; mixL, Laplacian distribution; mixt, t-distribution; TLE, trimmed likelihood estimation. The implemented algorithms includes: CTLERob stands for Component-wise adaptive Trimming Likelihood Estimation based mixture regression; mixbi stands for mixture regression based on bi-square estimation; mixLstands for mixture regression based on Laplacian distribution; TLE stands for Trimmed Likelihood Estimation based mixture regression. For more detail of the algorithms, please refer to below references. Reference: Chun Yu, Weixin Yao, Kun Chen (2017) . NeyKov N, Filzmoser P, Dimova R et al. (2007) . Bai X, Yao W. Boyer JE (2012) . Wennan Chang, Xinyu Zhou, Yong Zang, Chi Zhang, Sha Cao (2020) .


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1.1.0 by Wennan Chang, a year ago

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Authors: Sha Cao [aut, cph, ths] , Wennan Chang [aut, cre] , Chi Zhang [aut, ctb, ths]

Documentation:   PDF Manual  

GPL license

Imports flexmix, robustbase, gtools, MASS, methods, robust, lars, dplyr, rlang, scales, gplots, grDevices, graphics, RColorBrewer, stats, glmnet

See at CRAN