Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.02.233460v1?rss=1 Authors: Chang, W., Wan, C., Yu, C., Yao, W., Zhang, C., Cao, S. Abstract: Mixture regression has been widely used as a statistical model to untangle the latent subgroups of the sample population. Traditional mixture regression faces challenges when dealing with: 1) outli-ers and versatile regression forms; and 2) the high dimensionality of the predictors. Here, we de-velop an R package called RobMixReg, which provides comprehensive solutions for robust, flexible as well as high dimensional mixture modeling. Copy rights belong to original authors. Visit the link for more info