Abess

-------------

It is the second stable release for ``abess``. This version includes
multiple several generic features, and optimize memory usage when input
data is a sparse matrix. We also significantly enhancements to the
project’ documentation.

- Cpp

- New generic best subset features:

- The selection of group-structured best subset selection;
- Ridge-regularized penalty for parameter as a generic component.

- New best subset selection tasks:

- principal component analysis

- Performance improvement:

- Support sparse matrix as input
- Support golden section search for optimal support size. It is
much faster than sequentially searching strategy.
- The logic behind cross validation is optimized to gain speed
improvement
- Covariance update
- Bug fixed

- R package

- New best subset selection features and tasks implemented in Cpp
are wrapped in R functions.
- ``abesspca`` supports best subset selection for the first loading
vector in principal component analysis. A iterative algorithm
supports multiple loading vectors.
- Generic S3 function for ``abesspca``.
- Both ``abess`` and ``abesspca`` supports sparse matrix input
(inherit from class “sparseMatrix” as in package Matrix).
- Upload to CRAN.

- Python package

- New best subset selection features and tasks implemented in Cpp
are wrapped in Python functions.
- *abessPCA* supports best subset selection for the first loading
vector in principal component analysis. A iterative algorithm
supports multiple loading vectors.
- Support integration with ``scikit-learn``. It is compatible with
model evaluation and selection module with ``scikit-learn``.
- Initial Upload to Pypi.

- Project development

- Documentation

- A more clear project website layout.
- Add an instruction for
- Add tutorials to show simple use-cases and non-trival examples
of typical use-cases of the software.
- Link to R-package website.
- Add an instruction to help package development.

- Code coverage for line covering rate for Python.
- Continuous integration:

- Change toolbox from Travis CI to Github-Action.
- Auto deploy code coverage result to codecov.

-------------

We’re happy to announce the first major stable version of ``abess``.
This version includes multiple new algorithms and features. Here are
some highlights of the big updates.

- Cpp

- New generic best subset features:

- generic splicing technique
- nuisance selection

- New best subset selection tasks:

- linear regression
- logistic regression
- poisson regression
- cox proportional hazard regression
- multi-gaussian regression
- multi-nominal regression.

- Cross validation and information criterion to select the optimal
support size
- Performance improvement:

- Support OPENMP for the parallelism when performing cross
validation
- Warm start initialization

- Create a List object to: 1. facilitate transfer the data object
from Cpp to Python; 2. use the maximum compatible code for python
and R

- R package

- All best subset selection features and tasks implemented in Cpp
are wrapped in a R function ``abess``.
- Unified API for cross validation and information criterion to
select the optimal support size.
- Support generic S3 functions like ``coef`` and ``plot`` in R.
- A short vignettes for demonstrating the usage of package.
- Support formula interface.
- Support convenient function for generating synthetic dataset.
- Initial upload to CRAN.

- Python

- All best subset selection features implemented in Cpp are wrapped
in a Python according to tasks. For instance, *abessLm* supports
best subset selection for the linear model.
- Write the Python class on the basis of ``scikit-learn`` package.
The usage of the python package is the same as the common module
in ``scikit-learn``.
- Support convenient function for generating synthetic dataset in
Python.

- Project developing

- Build R package website via the ``pkgdown`` package.
- Build a documentation website on based the Python package via the
``sphnix`` package.
- The website is continuous integrated via Travis CI. The content
will automatically change whether a Travis CI is triggered.
- Complete testing for R functions in package.