• Initial version on CRAN.
  • Update CTLE function to CTLERob function. This new function have one more parameter ‘rlr_method’ which let user choose the robust regression method in ‘lmRob’,‘lmrob’,‘ltsReg’.

  • Update class definition of RobMixReg. The new class add one slot which return the posterior probability of the mixture regression.

  • Our paper published on arxiv, please cite us. For more detail of the proposed method, please refer to DESCRIPTION file.
  • Add method mixtureReg which let user run the mixture regression model flexibility by fixing the specific coefficient (slpoe value).

  • Add method Regression Based Suspace Learning (RBSL) that enable to fitting the mixture model for high dimension variable. The implementation is ‘CSMR’ function.

  • Add new wrapper function to deal with following four scenarios:

  1. robust regression: one regression line and outliers.

  2. flexible mixture regression: two regression lines without outliers. The flexible means that the coefficient of the regression line can be declare by the user.

  3. robust mixture regression: two regression lines with outliers. The algorithm to solve this challenge is Component-wise Adaptive Trimming (CAT). the implementation is ‘CTLERob’ function. The details of the algorithm please refer our paper.

  4. supervised subspace clustering: clustering heterogeneity objects into subgroup and selecting contributed attributes simultaneously. The algorithm to solve this challenge is RBSL. The details of the algorithm please refer our paper.

  • Add plot module for the above four scenarios.

  • Add two real dataset: Breast cancer multi-omic data and Two genes cytokine response data. The detail and example refer to package manual and vignette.

  • add several wrapper function

  • add model selection method

  • update vignette