Changes in version 0.1.6                        

Example Dataset Update

  - Replaced private example data (MEA022b/MEA022c, Mavs KO genotypes)
    with publicly available WT neuron data (MEA012, MEA013)
  - Example dataset now uses WT neurons with neuronal agonist
    treatments: PBS, KCl, DHPG, Gabazine (MEA012) and PBS, AMPA, KA,
    DHPG, Gabazine (MEA013)
  - Updated all example scripts
    (nova_quickstart.R, 01_compute.R, 02_plot.R,
    mea-analysis-example.Rmd) to reflect WT-only dataset structure
  - GENOTYPE_COLUMN now defaults to NULL in quickstart for WT-only
    datasets
  - Removed compiled HTML and figure outputs generated from private
    dataset

                 Changes in version 0.1.1 (2025-10-16)                  

CRAN Resubmission Fixes

  - Removed all commented code from examples and function bodies
  - Fixed DESCRIPTION to spell out Multi-Electrode Array (MEA) without
    quotes
  - Added newlines at end of all R source files
  - Fixed parse errors in documentation examples

Initial CRAN Release

This is the first release of NOVA (Neuroactivity Omics Visualization and
Analysis), a comprehensive toolkit for analyzing and visualizing
Multi-Electrode Array (MEA) data.

Main Features

  - Data Processing: Flexible MEA data processing with automatic
    structure discovery
    
      - discover_mea_structure(): Automatically detects data structure
      - process_mea_flexible(): Processes MEA data with quality
        filtering options
      - handle_missing_values(): Multiple strategies for handling
        missing data

  - Principal Component Analysis:
    
      - pca_analysis_enhanced(): Comprehensive PCA with visualization
      - analyze_pca_variable_importance_general(): Variable importance
        analysis
      - plot_pca_trajectories_general(): Trajectory visualization across
        conditions

  - Heatmap Visualization:
    
      - create_mea_heatmaps_enhanced(): Publication-ready heatmaps with
        multiple scaling options
      - Support for hierarchical clustering and custom annotations

  - Utility Functions: Helper functions for data manipulation, scaling,
    and color schemes

Documentation

  - Comprehensive function documentation with examples
  - Vignettes demonstrating typical workflows
  - Support for various MEA data formats