DOC: add software and sampling method citations

CHANGELOG.md

@@ -8,6 +8,8 @@ The format follows [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).
 
 ### Added
 
+- Software archive DOIs and algorithm references for SMC variants and flow
+  preconditioning.
 - Documentation sidebar links to sequince-density and sequince-examples under
   Related Packages.
 

CONTRIBUTING.md

@@ -296,7 +296,7 @@ should also follow this format and describe the final change.
 ## Pull requests and review
 
 For each pull request with user-visible changes, update
-[CHANGELOG.md](CHANGELOG.md) under `Unreleased` in the same pull request. Follow
+[CHANGELOG.md][changelog] under `Unreleased` in the same pull request. Follow
 the entry categories and guidance in the releases section below.
 
 Push your branch and open a pull request targeting `sequince-dev/sequince:main`:
@@ -323,7 +323,7 @@ needed.
 
 ## Releases
 
-Maintain [CHANGELOG.md](CHANGELOG.md) using
+Maintain [CHANGELOG.md][changelog] using
 [Keep a Changelog](https://keepachangelog.com/en/1.1.0/). Add user-visible changes
 under `Unreleased`, grouped as `Added`, `Changed`, `Deprecated`, `Removed`,
 `Fixed`, or `Security`; omit empty groups. Describe the effect on users,
@@ -349,3 +349,5 @@ built distribution in a clean environment. Publishing a GitHub release triggers
 the PyPI workflow in `.github/workflows/publish.yml`. Check that the workflow
 completes successfully. Published distributions cannot be overwritten, so verify
 the tag and artifacts before publishing.
+
+[changelog]: https://github.com/sequince-dev/sequince/blob/main/CHANGELOG.md

docs/citation.rst

@@ -1,8 +1,52 @@
 Citing sequince
 ===============
 
-If you use ``sequince`` in your work, please cite:
+Software citations
+------------------
 
-.. TODO: add citation details (e.g. paper reference and/or Zenodo DOI, BibTeX).
+If you use ``sequince``, please cite the Zenodo archive:
 
-*Citation details coming soon.*
+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.

docs/user_guide/running_inference/prebuilt_samplers.rst

@@ -13,14 +13,19 @@ Standard Bayesian SMC
 particle population through a sequence of tempered distributions, combining
 weighting, resampling, and mutation. It uses adaptive temperatures by default;
 ``adaptive=False`` with ``num_steps`` selects a fixed temperature schedule.
-The usual posterior endpoint is ``max_beta=1.0``.
+The usual posterior endpoint is ``max_beta=1.0``. For the SMC sampler
+framework, see `Del Moral, Doucet, and Jasra (2006)
+<https://doi.org/10.1111/j.1467-9868.2006.00553.x>`_.
 
 This is the helper used in the :ref:`quick start <quick-start>`. See
 :doc:`smc_configuration` for configuration and manual pipeline assembly.
 
 Persistent Bayesian SMC
 -----------------------
 
+Persistent sampling was introduced by `Karamanis and Seljak (2025)
+<https://doi.org/10.1007/s11222-025-10682-y>`_.
+
 :func:`~sequince.prebuilt_samplers.make_persistent_bayesian_smc` retains an
 archive of previous particle batches and reweights that archive as the
 temperature changes. ``n_particles`` sets the active batch size;
@@ -35,6 +40,12 @@ Set ``resample_at_end=False`` to return the complete weighted archive instead.
 Nested-sampling SMC
 -------------------
 
+Nested sampling via SMC (NS-SMC) follows the framework of
+`Salomone et al. (2025) <https://doi.org/10.1093/jrsssb/qkaf015>`_.
+The paper distinguishes fixed-threshold NS-SMC from adaptive NS-SMC (ANS-SMC).
+The helper uses adaptive thresholds by default; these do not inherit the
+fixed-schedule unbiasedness guarantee.
+
 :func:`~sequince.prebuilt_samplers.make_nested_sampling_smc` raises a
 likelihood threshold and explores the remaining constrained prior. It
 accumulates evidence over the discarded likelihood shells and the final
@@ -53,3 +64,14 @@ completed run and ``store_shell_particles=True`` (the default). See
 All three helpers expose mutation, resampling, and particle-transform options;
 see :doc:`../sampling_options/index` and their API references for supported settings.
 For standalone MCMC, see :doc:`../advanced_workflows/mcmc_sampler`.
+
+Waste-free SMC
+--------------
+
+Waste-free SMC, introduced by `Dau and Chopin (2022)
+<https://doi.org/10.1111/rssb.12475>`_, retains intermediate MCMC states within
+each stage. Assemble it in a custom pipeline with
+:class:`~sequince.steps.waste_free.WasteFreeResampleMove`; see the
+`worked tutorial <https://sequince.readthedocs.io/projects/sequince-examples/en/latest/_generated/examples/waste_free_smc/README.html>`_.
+Persistent sampling instead reuses populations across stages.
+Full method references are collected in :doc:`../../citation`.