Spatially Variable Genes Detection Methods for Spatial Transcriptomics // A unified framework for detecting spatially variable genes (SVGs) in spatial transcriptomics data. This package integrates multiple state-of-the-art SVG detection methods including 'MERINGUE' (Moran's I based spatial autocorrelation), 'Giotto' binSpect (binary spatial enrichment test), 'SPARK-X' (non-parametric kernel-based test), and 'nnSVG' (nearest-neighbor Gaussian processes). Each method is implemented with optimized performance through vectorization, parallelization, and 'C++' acceleration where applicable. Methods are described in Miller et al. (2021) doi:10.1101/gr.271288.120, Dries et al. (2021) doi:10.1186/s13059-021-02286-2, Zhu et al. (2021) doi:10.1186/s13059-021-02404-0, and Weber et al. (2023) doi:10.1038/s41467-023-39748-z.