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; 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>.
| Version: |
1.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
flexmix, robustbase, gtools, MASS, methods, robust, lars, dplyr, rlang, scales, gplots, grDevices, graphics, RColorBrewer, stats, glmnet |
| Published: |
2020-08-05 |
| DOI: |
10.32614/CRAN.package.RobMixReg |
| Author: |
Sha Cao [aut, cph, ths],
Wennan Chang [aut, cre],
Chi Zhang [aut, ctb, ths] |
| Maintainer: |
Wennan Chang <wnchang at iu.edu> |
| BugReports: |
https://github.com/changwn/RobMixReg/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL] |
| URL: |
https://changwn.github.io/RobMixReg/ |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
RobMixReg results |
Documentation:
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