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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "SVG" in publications use:'
type: software
license: MIT
title: 'SVG: Spatially Variable Genes Detection Methods for Spatial Transcriptomics'
version: 1.0.0
doi: 10.32614/CRAN.package.SVG
abstract: 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) <https://doi.org/10.1101/gr.271288.120>,
  Dries et al. (2021) <https://doi.org/10.1186/s13059-021-02286-2>, Zhu et al. (2021)
  <https://doi.org/10.1186/s13059-021-02404-0>, and Weber et al. (2023) <https://doi.org/10.1038/s41467-023-39748-z>.
authors:
- family-names: Liu
  given-names: Zaoqu
  email: liuzaoqu@163.com
  orcid: https://orcid.org/0000-0002-0452-742X
repository: https://cran.r-universe.dev
repository-code: https://github.com/Zaoqu-Liu/SVG
commit: a3ad09d0b5596bceefce81355b0b5aeba21edeba
url: https://zaoqu-liu.github.io/SVG/
date-released: '2026-02-01'
contact:
- family-names: Liu
  given-names: Zaoqu
  email: liuzaoqu@163.com
  orcid: https://orcid.org/0000-0002-0452-742X