Package: KODAMA
Title: Knowledge Discovery by Accuracy Maximization
Version: 0.99.7
Authors@R: c(
    person("Stefano", "Cacciatore", role = c("aut", "cre"),
           email = "stefano.cacciatore@icgeb.org",
           comment = c(ORCID = "0000-0001-7052-7156"))
  )
Author: Stefano Cacciatore [aut, cre]
Maintainer: Stefano Cacciatore <stefano.cacciatore@icgeb.org>
Description: Knowledge Discovery by Accuracy Maximization using KNN or
        SIMPLS followed by latent-space LDA. The portable source
        package vendors the MIT-licensed CPU implementation from the
        standalone 'kodama-cpp' library. Optional developer builds can
        link the same public interface to NVIDIA CUDA or Apple Metal
        backends. Reusable nearest-neighbor graphs, graph-aware
        visualization, preprocessing, and adapters for common
        single-cell containers are provided.
License: MIT + file LICENSE
Depends: R (>= 4.5.0)
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
LinkingTo: Rcpp
Imports: fastEmbedR (>= 0.99.5), methods, Rcpp, S4Vectors, stats
Suggests: BiocStyle, knitr, Matrix, rmarkdown, Seurat, SeuratObject,
        SingleCellExperiment, SpatialExperiment, SummarizedExperiment,
        testthat (>= 3.0.0)
VignetteBuilder: knitr
biocViews: Software, Classification, Clustering, DimensionReduction,
        GPU, Preprocessing, Visualization, SingleCell, Spatial
Config/testthat/edition: 3
SystemRequirements: C++17
URL: https://github.com/tkcaccia/KODAMA
BugReports: https://github.com/tkcaccia/KODAMA/issues
Config/roxygen2/version: 8.0.0
Repository: https://biocstaging.r-universe.dev
Date/Publication: 2026-08-21 09:47:07 UTC
RemoteUrl: https://github.com/BiocStaging/KODAMA
RemoteRef: HEAD
RemoteSha: f2934386d48f5af3cd1f25a845b3c6a55878e168
NeedsCompilation: yes
Packaged: 2026-09-30 11:13:54 UTC; root