NEWS
MeLSI 1.1.3
- Performance: additional ~22% speedup via diagonal vector storage in ensemble
aggregation (eliminates B x p^2 matrix allocation per permutation) and
rejection sampling in gradient optimizer (eliminates setdiff() allocation
per iteration). Combined with 1.1.2, total speedup is ~2.9x over 1.1.1.
MeLSI 1.1.2
- Performance: 2.3x faster permutation testing via vectorized prefiltering, direct
PERMANOVA F-statistic (replacing adonis2 overhead), and diagonal metric matrix
optimization (replacing O(p^3) eigen decomposition with O(p) column scaling).
Results are numerically identical; p-values unchanged.
- Suppress spurious NaN warnings from log2 fold-change on CLR-transformed data.
MeLSI 0.99.9
- Lower minimum R dependency to R >= 4.5.0 to match Bioconductor 3.23 build system
MeLSI 0.99.8
- Bump version to retrigger Bioconductor build report after CI configuration updates
MeLSI 0.99.7
- Switch CI to grimbough/bioc-actions to match the Bioconductor Build System environment
MeLSI 0.99.6
- Update CI to use R-devel and Bioconductor 3.23 targeting R >= 4.6.0
MeLSI 0.99.5
- Bump version to retrigger build after dependency updates
MeLSI 0.99.4
- Update minimum R dependency to R >= 4.6.0
MeLSI 0.99.3
- Address remaining Bioconductor reviewer checklist items for package acceptance
- Enable progress messages for pairwise comparisons in multi-group analysis
MeLSI 0.99.2
- Optimize vignette runtime to meet Bioconductor < 15 minute requirement
- Reduce example dataset size and permutation counts for faster vignette build
MeLSI 0.99.1
- Resolve Bioconductor pre-acceptance review issues: warnings, license NOTE, non-standard
directories, and build artifacts
- Add Matrix to dependencies
MeLSI 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)