Biobase, BiocGenerics, beadarray, RobLox(>= 1.1.0),
distrMod(>= 2.8.0), lattice, RColorBrewer, AnnotationDbi

c(person("Sha", "Cao", email = "shacao@iu.edu", role = c("aut", "cph","ths")),
person("Wennan", "Chang", email = "wnchang@iu.edu", role = c("aut", "cre")),
person("Chi", "Zhang", email = "czhang87@iu.edu", role = c("aut", "ctb","ths"))
)
Sha Cao [aut, cph, ths],
Wennan Chang [aut, cre],
Chi Zhang [aut, ctb, ths]
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>.
flexmix, robustbase, gtools, MASS, methods

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>.
Wennen Chang, Sha Cao, Chi Zhang (2019) <doi:10.1101/426593>.
flexmix, robustbase, gtools, MASS, methods,robust