If you use faissR in published work, please cite or acknowledge the package and the upstream vector-search systems it uses. faissR builds on FAISS for the FAISS CPU/GPU backends. Please also cite the FAISS project and relevant FAISS research papers for the indexes used in your analysis. For FAISS GPU indexes backed by NVIDIA cuVS, such as FAISS/cuVS IVF-Flat, IVF-PQ, and CAGRA, please acknowledge the Meta and NVIDIA FAISS/cuVS integration work. For direct cuVS HNSW, please acknowledge the RAPIDS cuVS HNSW API. CUHNSW is acknowledged as related Apache-2.0 CUDA HNSW prior software; faissR does not vendor or copy CUHNSW source.

Cacciatore S (2026). faissR: Fast Native Nearest Neighbours, Graphs, kNN Models, and k-means for R. https://github.com/tkcaccia/faissR

Meta FAIR. Faiss: A library for efficient similarity search and clustering of dense vectors. https://faiss.ai/index.html

Qi J, Szilvasy G, Norris M, Gandhi V (2025). Accelerating GPU indexes in Faiss with NVIDIA cuVS. Meta Engineering. https://engineering.fb.com/2025/05/08/data-infrastructure/accelerating-gpu-indexes-in-faiss-with-nvidia-cuvs/

RAPIDS Development Team (2026). cuVS HNSW C API documentation. https://docs.rapids.ai/api/cuvs/stable/c_api/neighbors_hnsw_c/

Kim J (2026). CUHNSW: CUDA implementation of Hierarchical Navigable Small World Graph algorithm. Apache-2.0 software. https://github.com/js1010/cuhnsw

Corresponding BibTeX entries:

  @Manual{,
    title = {faissR: Fast Native Nearest Neighbours, Graphs, kNN
      Models, and k-means for R},
    author = {Stefano Cacciatore},
    year = {2026},
    url = {https://github.com/tkcaccia/faissR},
  }

  @Manual{,
    title = {Faiss: A library for efficient similarity search and
      clustering of dense vectors},
    author = {Meta FAIR},
    year = {2026},
    url = {https://faiss.ai/index.html},
  }

  @Misc{,
    title = {Accelerating GPU indexes in Faiss with NVIDIA cuVS},
    author = {Junjie Qi and Gergely Szilvasy and Michael Norris and
      Vishal Gandhi},
    year = {2025},
    url =
      {https://engineering.fb.com/2025/05/08/data-infrastructure/accelerating-gpu-indexes-in-faiss-with-nvidia-cuvs/},
  }

  @Manual{,
    title = {cuVS HNSW C API documentation},
    author = {RAPIDS {Development Team}},
    year = {2026},
    url =
      {https://docs.rapids.ai/api/cuvs/stable/c_api/neighbors_hnsw_c/},
  }

  @Manual{,
    title = {CUHNSW: CUDA implementation of Hierarchical Navigable
      Small World Graph algorithm},
    author = {J. Kim},
    year = {2026},
    url = {https://github.com/js1010/cuhnsw},
  }