DOC: add software and flow method citations

CHANGELOG.md

@@ -8,6 +8,8 @@ The format follows [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).
 
 ### Added
 
+- Software archive DOI and method references for flow preconditioning and
+  masked autoregressive flows.
 - Documentation sidebar links to the main Sequince documentation and
   sequince-examples under Related Packages.
 

docs/index.rst

@@ -36,6 +36,21 @@ Features
    sequince <https://sequince.readthedocs.io/en/latest/>
    sequince-examples <https://sequince.readthedocs.io/projects/sequince-examples/en/latest/>
 
+Citing sequince-density
+-----------------------
+
+If you use ``sequince-density``, please cite the archived software:
+
+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 used in your analysis.
+Select that version on Zenodo and export its citation in your preferred
+format, using that record's DOI. Also cite the methods and
+backends used in your analysis; see :doc:`normalizing_flows` and :doc:`gaussian_mixtures`.
+For the sampling framework, follow `Sequince's citation guidelines
+<https://sequince.readthedocs.io/en/latest/citation.html>`_.
+
 Related projects
 ----------------
 

docs/normalizing_flows.rst

@@ -27,6 +27,13 @@ evaluate their log densities:
 
 ``ZukoFlow`` returns PyTorch tensors; ``FlowJax`` returns JAX arrays.
 
+The ``MAF`` architecture used here is the masked autoregressive flow of
+`Papamakarios, Pavlakou, and Murray (2017)
+<https://arxiv.org/abs/1705.07057>`_, *Masked Autoregressive Flow for Density
+Estimation*. Credit the architecture you select as well as the backend
+implementation: `Zuko <https://zuko.readthedocs.io/stable/>`_ or
+`FlowJAX <https://danielward27.github.io/flowjax/>`_.
+
 Particle transform restrictions
 -------------------------------
 
@@ -43,6 +50,12 @@ Nonperiodic transforms, such as ``AffineTransform``, remain supported.
 Preconditioned sampling
 -----------------------
 
+Flow-preconditioned SMC is described by `Karamanis et al. (2022)
+<https://doi.org/10.1093/mnras/stac2272>`_, *Accelerating astronomical and
+cosmological inference with Preconditioned Monte Carlo*. This is the method
+paper; the separate `pocoMC software paper
+<https://arxiv.org/abs/2207.05660>`_ describes that package.
+
 For preconditioned SMC, pass an unfitted flow to ``FlowTransform`` and use the
 transform as the ``particle_transform`` argument to
 ``make_standard_bayesian_smc``. The mutation step fits it to the current particle