Changes in version 1.0.0 (2026-02-01)                  

First Release

New Features

  - Unified interface: CalSVG() function provides a single entry point
    for all SVG detection methods
  - Six SVG detection methods implemented with consistent output format:
      - CalSVG_MERINGUE(): Moran's I with binary spatial network
      - CalSVG_Seurat(): Moran's I with inverse distance weights
      - CalSVG_binSpect(): Binary spatial enrichment test (Giotto)
      - CalSVG_SPARKX(): Non-parametric kernel-based test
      - CalSVG_nnSVG(): Nearest-neighbor Gaussian processes
      - CalSVG_MarkVario(): Mark variogram method

Spatial Network Construction

  - buildSpatialNetwork(): Build spatial neighborhood networks using
    Delaunay triangulation or KNN
  - getSpatialNeighbors_Delaunay(): Delaunay triangulation-based network
  - getSpatialNeighbors_KNN(): K-nearest neighbors network

Statistical Utilities

  - moranI(): Calculate Moran's I statistic
  - moranI_test(): Hypothesis testing for spatial autocorrelation
  - ACAT_combine(): Aggregated Cauchy Association Test for p-value
    combination
  - binarize_expression(): Multiple methods for expression binarization

Data Simulation

  - simulate_spatial_data(): Generate simulated spatial transcriptomics
    data with known SVGs
  - Support for multiple spatial patterns: gradient, hotspot, periodic,
    cluster

Performance

  - C++ implementation via Rcpp/RcppArmadillo for computationally
    intensive operations
  - Parallel processing support via n_threads parameter
  - Efficient memory usage for large-scale data

Documentation

  - Comprehensive vignette with mathematical background
  - Complete function documentation with examples
  - Benchmark comparison between methods

Dependencies

  - Core: Matrix, Rcpp, RcppArmadillo
  - Optional: geometry, RANN, BRISC, CompQuadForm, BiocParallel,
    spatstat

Authors

  - Zaoqu Liu (maintainer) - liuzaoqu@163.com