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) <doi:10.1002/cjs.11310>. NeyKov N, Filzmoser P, Dimova R et al. (2007) <doi:10.1016/j.csda.2006.12.024>. Bai X, Yao W. Boyer JE (2012) <doi:10.1016/j.csda.2012.01.016>. Wennan Chang, Xinyu Zhou, Yong Zang, Chi Zhang, Sha Cao (2020) <arXiv:2005.11599>.
|Author||Sha Cao [aut, cph, ths], Wennan Chang [aut, cre], Chi Zhang [aut, ctb, ths]|
|Maintainer||Wennan Chang <email@example.com>|
|Package repository||View on CRAN|
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