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) <arXiv:2005.11599>.
README.md
Robust Mixture Regression
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.