Description
Robust Mixture Regression.
Description
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) <doi:10.48550/arXiv.2005.11599>.
README.md
Robust Mixture Regression
Recent Update
A Python package of RobMixReg is developed by Hanming Ye and will be released soon.
Install from CRAN
install.packages("RobMixReg)
library("RobMixReg")
Install from github for most updated package.
Please report the bug as the description in the Question&Problem.
library("devtools")
devtools::install_github("changwn/RobMixReg")
Tutorial
A comprehensive and complete tutorial is here.
News
The package version control is in News.md
Citations
If you find the code helpful in your resarch or work, please cite us.
@article{wennan2020cat,
title={A New Algorithm using Component-wise Adaptive Trimming For Robust Mixture Regression},
author={Chang, Wennan and Wan, Changlin and Zhou, Xinyu and Zhang, Chi and Cao, Sha},
journal={arXiv preprint arXiv:2005.11599},
year={2020}
}
@article{chang2020supervised,
title={Supervised clustering of high dimensional data using regularized mixture modeling},
author={Chang, Wennan and Wan, Changlin and Zang, Yong and Zhang, Chi and Cao, Sha},
journal={arXiv preprint arXiv:2007.09720},
year={2020}
}
Questions & Problems
If you have any questions or problems, please feel free to open a new issue here. We will fix the new issue ASAP. You can also email the maintainers and authors below.
PhD candidate at Biomedical Data Research Lab (BDRL) , Indiana University School of Medicine.