• Adapt to the API change of the Matrix package.
  • Change the package structure such that the API functions can reuse the utility function. It facilitates the testing for package.
  • Update citation information.
  • Support generalized linear model for ordinal response, also named as rank learning in machine learning community.
  • Support robust principal analysis
  • Modify R package structure to make many internal components are reusable.
  • Update README.md
  • Support generalized linear model when the link function is Gamma distribution. By setting family = "gamma" in abess function, users can analyze the dataset with a positive valued and skewed response.
  • Support flexible support size for sequential principal component analysis (PCA), accompanied with several helpful generic function like plot.
  • Support user-specified cross validation division for abess and abesspca function by additional argument foldid.
  • Support robust principal component analysis now. A new R function abessrpca can access it.
  • Improve the R package document by: adding more details and giving more links related to core functions.
  • Add docs2search for R’s website
  • Support important searching to improve computational efficiency when dimension is 10,000.
  • Support sparse matrix as input
  • Support golden section search for optimal support size
  • Support ridge-regularized penalty as a generic component
  • Support group subset selection as a generic component
  • Best subset selection for principal component analysis via abesspca
  • Bug fixed
  • Initial stable version abess package
  • Support best subset selection for linear regression, logistic regression, poisson regression, cox proportional hazard regression, multi-gaussian regression, multi-nominal regression.
  • Support nuisance selection as a generic component
  • Unified API for cross validation and information criterion to select the optimal support size.
  • A documentation website is support for abess package