Changes in version 0.99.9                        

  - Lower minimum R dependency to R >= 4.5.0 to match Bioconductor 3.23
    build system

                       Changes in version 0.99.8                        

  - Bump version to retrigger Bioconductor build report after CI
    configuration updates

                       Changes in version 0.99.7                        

  - Switch CI to grimbough/bioc-actions to match the Bioconductor Build
    System environment

                       Changes in version 0.99.6                        

  - Update CI to use R-devel and Bioconductor 3.23 targeting R >= 4.6.0

                       Changes in version 0.99.5                        

  - Bump version to retrigger build after dependency updates

                       Changes in version 0.99.4                        

  - Update minimum R dependency to R >= 4.6.0

                       Changes in version 0.99.3                        

  - Address remaining Bioconductor reviewer checklist items for package
    acceptance
  - Enable progress messages for pairwise comparisons in multi-group
    analysis

                       Changes in version 0.99.2                        

  - Optimize vignette runtime to meet Bioconductor < 15 minute
    requirement
  - Reduce example dataset size and permutation counts for faster
    vignette build

                       Changes in version 0.99.1                        

  - Resolve Bioconductor pre-acceptance review issues: warnings, license
    NOTE, non-standard directories, and build artifacts
  - Add Matrix to dependencies

                       Changes in version 0.99.0                        

Initial Bioconductor Submission

  - Initial submission of MeLSI (Metric Learning for Statistical
    Inference) to Bioconductor
  - Novel machine learning method for microbiome beta diversity analysis
  - Learns optimal distance metrics to improve statistical power in
    detecting group differences
  - Comprehensive validation against standard methods (Bray-Curtis,
    Euclidean, Jaccard)
  - Robust ensemble learning approach with conservative pre-filtering
  - Validated on real microbiome datasets with proper Type I error
    control
  - Provides feature importance weights for biological interpretability
  - Includes helper functions for CLR transformation and visualization
    (VIP plots, PCoA)
  - Full integration with Bioconductor ecosystem (phyloseq, microbiome
    packages)