🎉 Minor Update with Package Updates & Stability Enhancements

[v0.0.4] - 2025-01-15 – Stability Enhancements & Package Updates

We’re excited to introduce Auto-Sklong v0.0.4! While this release is minor, it ensures that our dependencies remain up-to-date, particularly with the migration of Scikit-Longitudinal to version 0.0.7. This upgrade brings compatibility improvements and prepare the foundation for better performance in longitudinal machine learning tasks.

Highlights

1. Package Updates

   • Updated Scikit-Longitudinal to v0.0.7 to incorporate compatibility adjustments for the recent migration to uv.

2. Stability Improvements

   • Minor bug fixes and adjustments to ensure smoother workflows, especially with updated dependencies.

As always, thank you for your support and contributions! Let’s keep pushing forward together! 🚀

Previously in v0.0.3

🎄 Migration to UV & Documentation Improvements

We are glad to present Auto-Sklong v0.0.3. This new release focusses on streamlining the development workflow by migrating from PDM to uv, which speeds up installation and reduces complexity. Our documentation has also been updated, with clarifications to the Quick Start and new sections for Apple Silicon Mac customers. Most excitingly, our paper has been accepted to the IEEE BIBM 2024 conference—stay tuned for the BibTeX citation and additional publication details when the proceedings are posted!

Highlights

1. Migration from PDM to uv

   • Far simpler commands and fewer setup configurations for the community.
   • Substantial speed improvements, as shown in this benchmark comparison, pitting uv against poetry, PDM, and pip-sync.

2. Documentation Enhancements

   • Quick Start Fixes: Thanks to @anderdnavarro (in #4, #5, #6) for correcting parameter names in the Quick Start feature list examples.
   • Paper Acceptance: Our paper on Auto-Sklong has been accepted to the 2024 IEEE BIBM Conference. We will add the BibTeX reference once the proceedings are finalised.
   • Apple Silicon Installation Guide: The Quick Start now includes a dedicated section for installing Auto-Sklong on Apple Silicon-based Macs, making setup for M1/M2 systems more transparent and accessible. Thanks once more to @anderdnavarro for pointing that out!

Future Work

• BibTeX Citation: We will add a citation reference for our BIBM 2024 paper as soon as the proceedings are publicly available.
• Documentation: The experiments paper section will be simplified, and the redundant "Release History" tab in the documentation will be removed.
• Examples: We aim to launch a comprehensive Jupyter notebook tutorial to demonstrate how to use Auto-Sklong.

As always, thank you for your continued support. Let’s keep exploring the boundaries of longitudinal machine learning!

Merry XMas! 🎄