NEWS

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IntegratedLearner 0.99.2

Review-driven updates

• Revised package metadata for the current Bioconductor review cycle.
• Added Himel Mallick's ORCID to the Authors@R field in DESCRIPTION.
• Replaced the previous unformatted news file with a versioned NEWS.md.

Bioconductor workflow and documentation

• Reworked the package documentation and vignette to make MultiAssayExperiment the primary user-facing workflow.
• Expanded the vignette introduction to describe how IntegratedLearner fits into a typical Bioconductor pipeline.
• Updated examples and vignette code to use standard Bioconductor containers and accessors such as MultiAssayExperiment, SummarizedExperiment, experiments(), assay(), colData(), and sampleMap().
• Added package-level documentation and clarified return values for exported methods, including predict.learner().
• Revised man page examples to be runnable under package checking and removed blank if (FALSE) example blocks.
• Removed disabled vignette install chunks and switched installation snippets to plain code blocks to reduce the risk of documentation drift.

Dependencies and installation

• Reorganized dependencies so that packages needed at runtime are listed in Imports, while vignette/example/test-only packages are kept in Suggests where appropriate.
• Removed the runtime package-code dependence on stringr and replaced those uses with base R equivalents.
• Updated README installation instructions to include Bioconductor usage.

API and naming cleanup

• Continued aligning package-specific exported helper/learner names with snake_case conventions and reserving . for S3 methods.
• Preserved backward compatibility for older learner identifiers while supporting the newer naming style.

Examples, testing, and package checks

• Updated exported examples for IntegratedLearner, predict.learner(), update.learner(), and plot.learner() to improve reproducibility under R CMD check.
• Removed rendered vignette artifacts from vignettes/ and added ignore rules to avoid recommitting generated HTML outputs.
• Expanded and refreshed tests around model methods, MAE workflows, prediction/update behavior, and utility helpers.

Internal code quality

• Replaced the previous internal seed handling approach with a cleaner withr-based implementation.
• Updated print.learner() to return invisibly, consistent with base print() conventions.
• Reduced code repetition in the continuous/binary training, prediction, and update paths by introducing shared helper utilities for validation-layer prediction assembly, fusion prediction assembly, and family-specific metric calculation.