Changes in version 0.99.1                        

Changes

  - Updated DESCRIPTION to require R (>= 4.6.0).
  - Refactored repeated matrix transposition logic in trainRankModel()
    and predictRankModel() into an internal helper function
    (.transposeMatrix).
  - Reduced use of :: by importing selected functions (e.g., glmnet(),
    cv.glmnet()) to improve dependency checking.
  - Added package-level documentation (RankMap-package.R).
  - Updated vignette:
      - Removed GitHub installation instructions.
      - Added Bioconductor installation instructions using
        BiocManager::install("RankMap").
      - Added unique labels to all code chunks.

                       Changes in version 0.99.0                        

Initial submission to Bioconductor

New Features

  - Fast, robust, and scalable reference-based cell type annotation
    using multinomial regression on ranked expression matrices.
  - Supports both single-cell and spatial transcriptomics data.
  - Compatible with Seurat, SingleCellExperiment, and SpatialExperiment
    objects.
  - Core function RankMap() provides a streamlined pipeline for
    preprocessing, model training, and prediction.
  - Customizable preprocessing: top-K gene masking, optional binning,
    expression weighting, and scaling.
  - Additional functions:
      - computeRankedMatrix() – generate ranked matrices
      - trainRankModel() – train multinomial GLM
      - predictRankModel() – apply trained model to query data
      - evaluatePredictionPerformance() – assess accuracy
  - Optimized for large datasets with significantly faster runtime than
    SingleR, Azimuth, and RCTD.