Package: SVG
Type: Package
Title: Spatially Variable Genes Detection Methods for Spatial
        Transcriptomics
Version: 1.0.0
Authors@R: c(
    person("Zaoqu", "Liu", email = "liuzaoqu@163.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-0452-742X")),
    person("SVGbench", "Contributors", role = "ctb",
           comment = "Original method implementations"))
Description: 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>.
License: MIT + file LICENSE
URL: https://github.com/Zaoqu-Liu/SVG, https://zaoqu-liu.github.io/SVG/
BugReports: https://github.com/Zaoqu-Liu/SVG/issues
Encoding: UTF-8
LazyData: false
Depends: R (>= 4.0.0)
Imports: parallel, stats, utils, methods, MASS, Rcpp (>= 1.0.0)
Suggests: BRISC, geometry, RANN, CompQuadForm, BiocParallel,
        SpatialExperiment, SingleCellExperiment, SummarizedExperiment,
        spatstat.geom, spatstat.explore, testthat (>= 3.0.0), knitr,
        rmarkdown, covr
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.3.3
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2026-01-28 03:40:06 UTC; liuzaoqu
Author: Zaoqu Liu [aut, cre] (ORCID: <https://orcid.org/0000-0002-0452-742X>),
  SVGbench Contributors [ctb] (Original method implementations)
Maintainer: Zaoqu Liu <liuzaoqu@163.com>
Repository: CRAN
Date/Publication: 2026-02-01 07:20:07 UTC
Built: R 4.4.3; x86_64-w64-mingw32; 2026-02-23 13:09:14 UTC; windows
Archs: x64
