diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0647995..af05bb3 100644
--- a/CHANGELOG.md
+++ b/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.
diff --git a/docs/index.rst b/docs/index.rst
index b3e5437..73206a3 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -36,6 +36,21 @@ Features
sequince
sequince-examples
+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 `_.
+
+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
+`_.
+
Related projects
----------------
diff --git a/docs/normalizing_flows.rst b/docs/normalizing_flows.rst
index 32828b4..94ba6ad 100644
--- a/docs/normalizing_flows.rst
+++ b/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)
+`_, *Masked Autoregressive Flow for Density
+Estimation*. Credit the architecture you select as well as the backend
+implementation: `Zuko `_ or
+`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)
+`_, *Accelerating astronomical and
+cosmological inference with Preconditioned Monte Carlo*. This is the method
+paper; the separate `pocoMC software paper
+`_ 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