RobMixReg: 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; CTLE, component-wise adaptive trimming likelihood estimation; mixbi, bi-square estimation; mixL, Laplacian distribution; mixt, t-distribution; TLE, trimmed likelihood estimation. The implemented algorithms includes: CTLE 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>. Wennen Chang, Sha Cao, Chi Zhang (2019) <doi:10.1101/426593>.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: flexmix, robustbase, gtools, MASS, methods
Published: 2020-03-25
Author: Sha Cao [aut, cph, ths], Wennan Chang [aut, cre], Chi Zhang [aut, ctb, ths]
Maintainer: Wennan Chang <wnchang at>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
Materials: README NEWS
CRAN checks: RobMixReg results


Reference manual: RobMixReg.pdf
Package source: RobMixReg_0.1.0.tar.gz
Windows binaries: r-prerelease:, r-release:, r-oldrel:
macOS binaries: r-prerelease: RobMixReg_0.1.0.tgz, r-release: RobMixReg_0.1.0.tgz, r-oldrel: not available


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