RobMixReg

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.

Last 30 days

This package has been downloaded 138 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 7 times.

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Last 365 days

This package has been downloaded 2,155 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Jul 21, 2024 with 74 downloads.

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