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abess 0.4.9

• Fix bug in Cpp level
• Fix error in: https://www.stats.ox.ac.uk/pub/bdr/clang19/abess.log
• Fix notes in https://cran.r-project.org/web/checks/check_results_abess.html

abess 0.4.8

• Support no-intercept GLM model by param ‘fit.intercept’.
• Allow to restrict the range of estimation for beta by param ‘beta.high’ and ‘beta.low’.
• Add cite message when load ‘abess’.
• Fix a bug when support.size is 0.

abess 0.4.7

• Allow the other criterion for model selection: AUC for (multinomial) logistic regression such as the area under the curve (AUC).
• Simplify the C++ code structure.
• Fix note “Specified C++11: please update to current default of C++17” in CRAN.

abess 0.4.6

• 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.

abess 0.4.5

• 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

abess 0.4.0

• 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.

abess 0.3.0

• Add docs2search for R’s website
• Support important searching to improve computational efficiency when dimension is 10,000.

abess 0.2.0

• 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

abess 0.1.0

• 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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.