# ------------------------------------------------
# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "faissR" in publications use:'
type: software
license: MIT
title: 'faissR: FAISS-Backed Nearest Neighbours, Graph Clustering, kNN Models, and
  k-Means'
version: 0.99.14
identifiers:
- type: doi
  value: 10.32614/CRAN.package.faissR
abstract: Native nearest-neighbour search, graph construction, graph clustering, k-nearest
  neighbour prediction, and k-means helpers for large dimensionality reduction workflows
  in high-throughput biological data analysis, including single-cell, flow cytometry,
  imaging, and mass spectrometry applications. The package requires the FAISS C++
  library for all builds, including CPU-only indexes, and can optionally use FAISS
  GPU indexes with NVIDIA cuVS integration or direct RAPIDS cuVS/CUDA indexes on NVIDIA
  machines. CPU-only systems do not need NVIDIA libraries, but NVIDIA CUDA/cuVS libraries
  are mandatory when a GPU-enabled build is explicitly requested. Supervised kNN models
  use knn(Xtrain, Ytrain) and predict(), or knn(Xtrain, Ytrain, Xtest) for immediate
  prediction. Class probabilities are returned through predict(type = "prob") for
  kNN classification models.
authors:
- family-names: Cacciatore
  given-names: Stefano
  email: tkcaccia@gmail.com
  orcid: https://orcid.org/0000-0001-7052-7156
preferred-citation:
  type: manual
  title: 'faissR: Fast Native Nearest Neighbours, Graphs, kNN Models, and k-means
    for R'
  authors:
  - family-names: Cacciatore
    given-names: Stefano
    email: tkcaccia@gmail.com
    orcid: https://orcid.org/0000-0001-7052-7156
  year: '2026'
  url: https://github.com/tkcaccia/faissR
repository: https://biocstaging.r-universe.dev
repository-code: https://github.com/tkcaccia/faissR
commit: 9f626aa0ea2a37188e15753a47d21dde64154815
url: https://github.com/tkcaccia/faissR
date-released: '2026-07-10'
contact:
- family-names: Cacciatore
  given-names: Stefano
  email: tkcaccia@gmail.com
  orcid: https://orcid.org/0000-0001-7052-7156
references:
- type: manual
  title: 'Faiss: A library for efficient similarity search and clustering of dense
    vectors'
  authors:
  - family-names: FAIR
    given-names: Meta
  year: '2026'
  url: https://faiss.ai/index.html
- type: generic
  title: Accelerating GPU indexes in Faiss with NVIDIA cuVS
  authors:
  - family-names: Qi
    given-names: Junjie
  - family-names: Szilvasy
    given-names: Gergely
  - family-names: Norris
    given-names: Michael
  - family-names: Gandhi
    given-names: Vishal
  year: '2025'
  url: https://engineering.fb.com/2025/05/08/data-infrastructure/accelerating-gpu-indexes-in-faiss-with-nvidia-cuvs/
- type: manual
  title: cuVS HNSW C API documentation
  authors:
  - family-names: Development Team
    given-names: RAPIDS
  year: '2026'
  url: https://docs.rapids.ai/api/cuvs/stable/c_api/neighbors_hnsw_c/
- type: manual
  title: 'CUHNSW: CUDA implementation of Hierarchical Navigable Small World Graph
    algorithm'
  authors:
  - family-names: Kim
    given-names: J.
  year: '2026'
  url: https://github.com/js1010/cuhnsw