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

A BibTeX entry for LaTeX users is

  @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},
  }

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

A BibTeX entry for LaTeX users is

  @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},
  }

  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/

A BibTeX entry for LaTeX users is

  @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/},
  }

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

A BibTeX entry for LaTeX users is

  @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/},
  }

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

A BibTeX entry for LaTeX users is

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