Citing sequince
===============

Software citations
------------------

If you use ``sequince``, please cite the Zenodo archive:

Williams, M. J., and Chapman-Bird, C. (2026). *sequince-dev/sequince*.
Zenodo. `doi:10.5281/zenodo.23596103 <https://doi.org/10.5281/zenodo.23596103>`_.

If you also use ``sequince-density``, please cite its Zenodo archive:

Williams, M. J. (2026). *sequince-dev/sequince-density*.
Zenodo. `doi:10.5281/zenodo.23687687 <https://doi.org/10.5281/zenodo.23687687>`_.

Ideally, cite the Zenodo record for the exact version of each package used
in your analysis. Select that version on Zenodo and export its citation
in your preferred format.

Algorithm references
--------------------

Please also cite the methods used in your analysis. These references credit
algorithm development; they are separate from the software citation.

* **SMC samplers:** Del Moral, P., Doucet, A., and Jasra, A. (2006).
  *Sequential Monte Carlo samplers*. Journal of the Royal Statistical Society:
  Series B, 68(3), 411–436.
  `doi:10.1111/j.1467-9868.2006.00553.x <https://doi.org/10.1111/j.1467-9868.2006.00553.x>`_.
* **Persistent sampling:** Karamanis, M., and Seljak, U. (2025).
  *Persistent sampling: enhancing the efficiency of sequential Monte Carlo*.
  Statistics and Computing, 35, 144.
  `doi:10.1007/s11222-025-10682-y <https://doi.org/10.1007/s11222-025-10682-y>`_.
* **Waste-free SMC:** Dau, H.-D., and Chopin, N. (2022).
  *Waste-free sequential Monte Carlo*. Journal of the Royal Statistical Society:
  Series B, 84(1), 114–148.
  `doi:10.1111/rssb.12475 <https://doi.org/10.1111/rssb.12475>`_.
* **Nested sampling via SMC (NS-SMC):** Salomone, R., South, L. F.,
  Drovandi, C., Kroese, D. P., and Johansen, A. M. (2025).
  *Unbiased and consistent nested sampling via sequential Monte Carlo*.
  Journal of the Royal Statistical Society Series B: Statistical Methodology,
  87(4), 1221–1238.
  `doi:10.1093/jrsssb/qkaf015 <https://doi.org/10.1093/jrsssb/qkaf015>`_.
* **Flow-preconditioned SMC:** Karamanis, M., Beutler, F., Peacock, J. A.,
  Nabergoj, D., and Seljak, U. (2022).
  *Accelerating astronomical and cosmological inference with Preconditioned
  Monte Carlo*.
  `doi:10.1093/mnras/stac2272 <https://doi.org/10.1093/mnras/stac2272>`_.

For mutation backends, density estimators, and flow architectures, also follow
the citation guidance of the packages and methods used.