Changes in version 0.99.0                        

🚀 Enhanced Parameter Support & Internal Dependencies

New Features

  - Enhanced Parameter Support: All main functions now expose the
    complete parameter sets from underlying tools:
      - infer_networks(): Now supports all method-specific parameters
        (GENIE3, GRNBoost2, ZILGM, JRF, PCzinb)
      - create_consensus(): Now supports all INetTool parameters
        (tolerance, nitermax, verbose)
      - community_path(): Now supports all robin package parameters
        (method-specific args, comparison params, parallel processing)
  - Verbose Output: Added verbose parameter to track progress across all
    functions
  - Seed Support: Added seed parameter for reproducible results
  - Internal Dependencies: Integrated ZILGM and JRF functions internally
    with proper GPL-2 attribution

Internal Dependencies Integration

  - ZILGM Functions: Integrated find_lammax() and zilgm() functions from
    ZILGM package (Park et al., 2021)
  - JRF Functions: Added fallback implementation for JRF functionality
    when package not available
  - INet-Tool: Maintained as external dependency (already in imports)
  - License Update: Changed to GPL (>= 2) to accommodate integrated
    GPL-2 code

Breaking Changes

  - License changed from MIT to GPL (>= 2) due to integrated GPL-2 code
  - ZILGM no longer required as external dependency (functions
    integrated)
  - JRF now optional with internal fallback implementation

Bug Fixes & Improvements

  - Improved parameter validation and error handling
  - Better documentation with comprehensive parameter descriptions
  - Enhanced examples showing new parameter capabilities

🚀 Initial Release

  - Released the first development version of scGraphVerse.
  - Provides gene regulatory network (GRN) inference from single-cell
    RNA-seq data.
  - Supports multiple network inference methods:
      - GENIE3 (tree-based ensemble method)
      - GRNBoost2 (Python-based gradient boosting)
      - ZILGM (zero-inflated Gaussian graphical model)
      - PCzinb (partial correlation with zero-inflated negative binomial
        model)
      - JRF (joint random forests for multi-dataset inference)

✨ Major Features

  - infer_networks(): Infer regulatory networks from count matrices.
  - cutoff_adjacency(): Apply null model thresholding to weighted
    adjacency matrices.
  - create_consensus(): Build consensus networks across methods or
    datasets.
  - plotROC(), community_similarity(): Performance evaluation tools
    using ROC curves, AUC, and community structure metrics.
  - plotg(), community_path(): Network visualization functions based on
    ggraph.

🧪 Testing and Documentation

  - Added runnable examples to all major exported functions.
  - Built a comprehensive README and External Dependencies installation
    guide.
  - Set up internal unit tests for core network inference and evaluation
    functionalities.
  - Prepared detailed documentation with reproducible examples.

🏛 Project Funding

  - Supported by the National Centre for HPC, Big Data and Quantum
    Computing under the European Union – Next Generation EU – CN00000013
    (CUP: B93C22000620006).