abess implements a unified framework of best-subset selection for solving diverse machine learning problems, e.g., linear regression, classification, and principal component analysis. Particularly, abess certifiably gets the optimal solution within polynomial time with high probability under the linear model. Our efficient implementation allows abess to attain the solution of best-subset selection problems as fast as or even 20-times faster than existing competing variable (model) selection toolboxes. Furthermore, it supports common variants like best subset of groups selection and ridge-regularized best-subset selection. The core of the library is programmed in C++. For ease of use, a Python library is designed for convenient integration with sklearn, and it can be installed from the Python Package Index (PyPI). In addition, a user-friendly R library is available at the Comprehensive R Archive Network (CRAN). The full
documentation is online accessible.