scikit-bio: a community-driven Python library for bioinformatics, providing versatile data structures, algorithms and educational resources.
We are excited to announce scikit-bio 0.6.0!
This release introduces significant enhancements and features, including the launch of a new website, the addition of new modules, classes and algorithms, and support for the latest versions of Python and SciPy. This release also includes various optimizations, bug fixes, and documentation improvements, making it a substantial upgrade for users and developers alike.
Release highlights:
Review the changelog for a complete list of the changes. Browse the documentation to learn about what you can do with scikit-bio. Follow @scikitbio for project updates. Thanks for your interest in scikit-bio!
We are excited to announce scikit-bio 0.5.9!
You can review the CHANGELOG.md for a complete description of the changes in this release. To learn about what you can do with scikit-bio, browse our API documentation.
Be sure to follow @scikitbio on twitter for project updates, and as always, thanks for your interest in scikit-bio!
We are excited to announce scikit-bio 0.5.8!
You can review the CHANGELOG.md for a complete description of the changes in this release. To learn about what you can do with scikit-bio, browse our API documentation.
Be sure to follow @scikitbio on twitter for project updates, and as always, thanks for your interest in scikit-bio!
We are very excited to announce scikit-bio 0.5.7!
You can review the CHANGELOG.md for a complete description of the changes in this release. To learn about what you can do with scikit-bio, browse our API documentation.
Be sure to follow @scikitbio on twitter for project updates, and as always, thanks for your interest in scikit-bio!
We are very excited to announce scikit-bio 0.5.6.
You can review the CHANGELOG.md for a complete description of the changes in this release. To learn about what you can do with scikit-bio, browse our API documentation.
Be sure to follow @scikitbio on twitter for project updates, and as always, thanks for your interest in scikit-bio!
We are very excited to announce scikit-bio 0.5.5. This is a minor release that adds a couple of compositional techniques under skbio.stats.composition namely the alr, and inverse alr transform in addition to easier construction of balance basis through sequential binary partitioning.
You can review the CHANGELOG.md for a complete description of the changes in this release. To learn about what you can do with scikit-bio, browse our API documentation.
Be sure to follow @scikitbio on twitter for project updates, and as always, thanks for your interest in scikit-bio!
We are very excited to announce scikit-bio 0.5.4. This is a minor release that adds a heuristic-based method to calculate PCoA. For large distance matrices, this option will dramatically reduce the memory footprint and accelerate the compute in skbio.stats.ordination.pcoa.
You can review the CHANGELOG.md for a complete description of the changes in this release. To learn about what you can do with scikit-bio, browse our API documentation.
Be sure to follow @scikitbio on twitter for project updates, and as always, thanks for your interest in scikit-bio!
We are very excited to announce scikit-bio 0.5.3. This is a minor release that adds a few new features in different modules of scikit-bio. Most notably it adds a few new methods to TreeNode, the permdisp test (to test for homogeneity of dispersions in distance-based comparisons), and pcoa_biplot to create biplots from an existing PCoA matrix.
You can review the CHANGELOG.md for a complete description of the changes in this release. To learn about what you can do with scikit-bio, browse our API documentation.
Be sure to follow @scikitbio on twitter for project updates, and as always, thanks for your interest in scikit-bio!
We're very excited to announce scikit-bio 0.5.2.
You can review the CHANGELOG.md for a complete description of the changes in this release. To learn about what you can do with scikit-bio, you should browse our API documentation.
Be sure to follow @scikitbio on twitter for project updates, and as always, thanks for your interest in scikit-bio!
We're very excited to announce scikit-bio 0.5.1. scikit-bio versions 0.5.0 and greater are no longer compatible with Python 2. scikit-bio is compatible with Python 3.4 and later. This is a minor beta release adding support for efficiently storing interval metadata on scikit-bio objects (similar to .metadata/.positional_metadata). This release also contains a number of minor new features and bug fixes.
You can review the CHANGELOG.md for a complete description of the changes in this release. To learn about what you can do with scikit-bio, browse our API documentation.
Be sure to follow @scikitbio on twitter for project updates, and as always, thanks for your interest in scikit-bio!