The Stan Blog
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date: 2026-05-19T14:59:05Z
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items: Release of CmdStan 2.39
items: Release of CmdStan 2.38
items: StanCon 2026, Uppsala, Sweden
items: Release of CmdStan 2.37
items: Release of CmdStan 2.36
items: From sampling statement to distribution statement
items: Release of CmdStan 2.35
items: Release of CmdStan 2.34
items: Release of CmdStan 2.33
items: Release of CmdStan 2.32

Release of CmdStan 2.39
type: item
creator: ward_brianm
date: 2026-05-19T14:59:05Z
subject: Uncategorized
description: We are very happy to announce that the 2.39.0 release of CmdStan is now available on Github! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. This release cycle brings the embedded Laplace approximation, new functions, and many fixes. Contributors, sponsors and donators We would like … Continue reading Release of CmdStan 2.39 →
encoded: We are very happy to announce that the 2.39.0 release of CmdStan is now available on Github! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. This release cycle brings the embedded Laplace approximation, new functions, and many fixes. Contributors, sponsors and donators We would like to thank everyone that contributed to this release with their bug reports, feature or bug fix discussions, and code or code reviews. Several issues in early versions of this release were caught and reported by the community, and we are grateful for their time and effort in improving Stan. We would also like to thank all of our sponsors and donators who have supported us during this last release cycle and in years past. Thanks to the Flatiron Institute for providing us with the computing resources to run unit tests and build scripts for our repositories. You can support Stan via NumFocus or the Github sponsorship program. Release highlights The embedded Laplace approximation After a few additional improvements to the underlying algorithm, we’re releasing a suite of functions to perform an embedded Laplace approximation, in a similar flavor to what is done in the INLA and TMB packages. These functions approximate the marginal likelihoods and conditional posteriors that arise in latent Gaussian models. The idea is to integrate out the latent Gaussian variables with a Laplace approximation and then perform standard inference on the hyperparameters. These functions give users a lot of flexibility when specifying a prior covariance matrix and a likelihood function, although the approximation is not guaranteed to work well for an arbitrary likelihood. Documentation for the embedded Laplace approximation is available here and here. Some additional materials are available here, though please note that the syntax has changed since these older materials were written. This is a large feature, with a lot of credit to spread around. Particular thank…

Release of CmdStan 2.38
type: item
creator: ward_brianm
date: 2026-01-13T17:50:14Z
subject: Uncategorized
description: We are very happy to announce that the 2.38.0 release of CmdStan is now available on Github! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. This release cycle focused on stability and usability improvements, with a few new features and a lot of work under … Continue reading Release of CmdStan 2.38 →
encoded: We are very happy to announce that the 2.38.0 release of CmdStan is now available on Github! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. This release cycle focused on stability and usability improvements, with a few new features and a lot of work under the hood for upcoming features. Contributors, sponsors and donators We would like to thank everyone that contributed to this release with their bug reports, feature or bug fix discussions, and code or code reviews. Several issues in early versions of this release were caught and reported by the community, and we are grateful for their time and effort in improving Stan. We would also like to thank all of our sponsors and donators who have supported us during this last release cycle and in years past. Thanks to the Flatiron Institute for providing us with the computing resources to run unit tests and build scripts for our repositories. You can support Stan via NumFocus or the Github sponsorship program. Release highlights Compiler usability improvements The Stan compiler’s job is primarily to turn Stan code into C++ code, but a very close second is to turn invalid Stan code into error messages. Both of these functionalities saw improvements this version. The C++ code generation saw several changes to try to improve compatibility with different compilers, avoid bad numeric properties that can arise when mixing integers into function calls expecting reals, and avoid some issues for user-defined functions that accepted complicated tuples or used names that are common in other C++ code. On the warning-and-error front, almost all the messages that the compiler can emit were given a once-over for wording consistency and clarity. Additionally, supported terminals can now display the messages in color! <img width="1024" height="447" data-attachment-id="578" data-permalink="https://blog.mc-stan.org/2026/01/13/release-of-cmdstan-2-38/image-2/" data-orig-file="https://blog.m…

StanCon 2026, Uppsala, Sweden
type: item
creator: avehtari
date: 2025-10-13T14:54:00Z
subject: Event
description: We are thrilled to announce that StanCon 2026 will take place in Uppsala, Sweden, from August 17th to August 21st, 2026. The conference brings together researchers and practitioners passionate about Bayesian inference and probabilistic programming in one of Sweden’s most historic and vibrant university cities. Attendees will enjoy a week of conference talks, workshops, and … Continue reading StanCon 2026, Uppsala, Sweden →
encoded: We are thrilled to announce that StanCon 2026 will take place in Uppsala, Sweden, from August 17th to August 21st, 2026. The conference brings together researchers and practitioners passionate about Bayesian inference and probabilistic programming in one of Sweden’s most historic and vibrant university cities. Attendees will enjoy a week of conference talks, workshops, and tutorials spanning both foundational methods and real-world applications. Stay tuned for updates! More information about registration, abstract submission, the program, and deadlines will be posted at https://www.stancon2026.org/. The conference is sponsored by Uppsala University, The Swedish Excellence Centre for Computational Social Science at Linköping University, eSSENCE, and Beijerstiftelsen. With kind regards from the organising committee Måns Magnusson (Uppsala University) Sara Hamis (Uppsala University) Aki Vehtari (Aalto University)

Release of CmdStan 2.37
type: item
creator: ward_brianm
date: 2025-09-02T16:20:11Z
subject: Uncategorized
description: We are very happy to announce that the 2.37.0 release of CmdStan is now available on Github! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. This release cycle brings improvements to Stan’s constrained types, quality-of-life improvements to CmdStan, and many stability improvements and bug fixes. … Continue reading Release of CmdStan 2.37 →
encoded: We are very happy to announce that the 2.37.0 release of CmdStan is now available on Github! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. This release cycle brings improvements to Stan’s constrained types, quality-of-life improvements to CmdStan, and many stability improvements and bug fixes. Contributors, sponsors and donators We would like to thank everyone that contributed to this release with their bug reports, feature or bug fix discussions, and code or code reviews. Several issues in early versions of this release were caught and reported by the community, and we are grateful for their time and effort in improving Stan. We would also like to thank all of our sponsors and donators who have supported us during this last release cycle and in years past. Thanks to the Flatiron Institute for providing us with the computing resources to run unit tests and build scripts for our repositories. You can support Stan via NumFocus or the Github sponsorship program. Release highlights New and Improved Constraints Similar to last release’s sum_to_zero_vector, this release features a new type, the sum_to_zero_matrix. This type sums to zero across both rows and columns. Additionally, the existing simplex, column_stochastic_matrix, and row_stochastic_matrix types have been updated to use a new transformation under the hood, which should be faster and more stable in different posterior geometries. Finally, building on the jacobian += statement, and _jacobian functions added in Stan 2.36, this release adds new functions that expose the implementations of Stan’s built-in constraints to the user. Each of the built-in transforms now has three corresponding functions: <transform>_unconstrain – applies the (unconstraining) transform <transform>_constrain – applies the inverse (constraining) transform <transform>_jacobian – applies the inverse (constraining) transform and increments the Jacobian adjustment term accordingly CmdStan…

Release of CmdStan 2.36
type: item
creator: ward_brianm
date: 2024-12-10T14:35:06Z
subject: Uncategorized
description: We are very happy to announce that the 2.36.0 release of CmdStan is now available on Github! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. This release cycle brings new constrained types, improved diagnostics, and a new distribution for heavy-tailed count data. Contributors, sponsors and … Continue reading Release of CmdStan 2.36 →
encoded: We are very happy to announce that the 2.36.0 release of CmdStan is now available on Github! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. This release cycle brings new constrained types, improved diagnostics, and a new distribution for heavy-tailed count data. Contributors, sponsors and donators We would like to thank everyone that contributed to this release with their bug reports, feature or bug fix discussions, and code or code reviews. Several issues in early versions of this release were caught and reported by the community, and we are grateful for their time and effort in improving Stan. We would also like to thank all of our sponsors and donators who have supported us during this last release cycle and in years past. Thanks to the Flatiron Institute for providing us with the computing resources to run unit tests and build scripts for our repositories. You can support Stan via NumFocus or the Github sponsorship program. Release highlights New constraints for stochastic matrices and zero-sum vectors Three new constrained types have been added to Stan in this release: column_stochastic_matrix: A matrix where each column is a simplex row_stochastic_matrix: A matrix where each row is a simplex sum_to_zero_vector: A vector constrained to sum to zero Easier user-defined constraints Historically, users have implemented their own constraints by using target += or _lp functions to add the required Jacobian adjustments. This release adds a new statement, jacobian +=, and accompanying _jacobian functions to make it easier to implement user-defined constraints. Besides being clearer in their intent, these new forms also allow the jacobian to be turned off when it is not needed, such as when finding a maximum likelihood estimate via optimization. Improved diagnostics The stansummary command bundled with CmdStan has been updated with newer versions of the R-hat and ESS diagnostics based on the work in Rank-Normalizatio…

From sampling statement to distribution statement
type: item
creator: avehtari
date: 2024-06-12T18:35:46Z
subject: Documentation
encoded: The sampling statement name for tilde (~) statements in Stan documentation has been replaced with distribution statement. The background is the tilde (~) statement does not do any sampling (but due to the sampling statement name, some people had wrongly assumed so, which caused confusion) in the literature, tilde (~) is usually read “is distributed as” right side of the tilde (~) statement can only be a built-in or user defined distribution distribution statement makes it more natural to discuss the difference in defining the model with collection of distributions or with log density increments The change affects only the documentation. The documentation has been revised to be more clear on differences in describing models with distribution statements and increment log density (target +=) statements. As this blog lacks equations support, it’s best to go to read the updated documentation on distribution statement. In addition, Stan User’s Guide sections on censored data models and zero-inflated count models are good examples illustrating the difference between describing a data model with distribution statement or writing the likelihood directly with increment log density statement.

Release of CmdStan 2.35
type: item
creator: ward_brianm
date: 2024-06-03T15:25:53Z
subject: Uncategorized
description: We are very happy to announce that the 2.35.0 release of CmdStan is now available on Github! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. A PyStan update will follow shortly. This release cycle focused on fixing bugs and adding quality of life improvements to … Continue reading Release of CmdStan 2.35 →
encoded: We are very happy to announce that the 2.35.0 release of CmdStan is now available on Github! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. A PyStan update will follow shortly. This release cycle focused on fixing bugs and adding quality of life improvements to the Stan ecosystem. Contributors, sponsors and donators We would like to thank everyone that contributed to this release with their bug reports, feature or bug fix discussions, and code or code reviews. Several issues in early versions of this release were caught and reported by the community, and we are grateful for their time and effort in improving Stan. We would also like to thank all of our sponsors and donators who have supported us during this last release cycle and in years past. Thanks to the Flatiron Institute for providing us with the computing resources to run unit tests and build scripts for our repositories. You can support Stan via NumFocus or the Github sponsorship program. Release highlights Bail out with fatal_error() There is a new statement type: fatal_error. This looks a lot like print or reject, but will immediately terminate the algorithm rather than being used to signal a divergence. A new RNG used internally Stan now uses the boost::mixmax pRNG for generating random numbers internally. The primary consequence of this for most users is that seeds from previous versions will not yield the same numerical results on this version. As per usual, exact reproducibility requires a fixed version of Stan, but this change is larger than normal, hence it being noted here. Improved CmdStan outputs Several new output options are available for CmdStan to produce JSON files for easier processing. – The diagnostic_file argument for the Laplace approximation sampler will now give a JSON output which contains the Hessian of the mode – The laplace method now supports the calculate_lp argument, similar to Pathfinder in the previous release. – Pathfinder…

Release of CmdStan 2.34
type: item
creator: rokcesnovar
date: 2024-01-16T17:50:51Z
subject: Uncategorized
description: We are very happy to announce that the 2.34.0 release of CmdStan is now available on GitHub! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. A PyStan update will follow shortly. This new release brings several improvements to the headline features of the last release … Continue reading Release of CmdStan 2.34 →
encoded: We are very happy to announce that the 2.34.0 release of CmdStan is now available on GitHub! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. A PyStan update will follow shortly. This new release brings several improvements to the headline features of the last release – tuple types and the Pathfinder algorithm, new distributions, and other improvements. Contributors, sponsors and donators We would like to thank everyone that contributed to this release with their bug reports, feature or bug fix discussions, and code or code reviews. We would also like to thank all of our sponsors and donators who have supported us during this last release cycle and in years past. Thanks to the Flatiron Institute for providing us with the computing resources to run unit tests and build scripts for our repositories. You can support Stan via NumFocus or the Github sponsorship program. Release highlights Tuple “unpacking” A new feature has been added that will be familiar to users of Python and other languages. Tuple-like items can now appear on the left-hand side of an = in assignment, in a feature known as “unpacking”. See docs for more. For example, the following is now valid: parameters { matrix[N, M] A; } model { matrix[N, M] Q; matrix[M, M] R; (Q, R) = qr_thin(A); // qr_thin returns a tuple(matrix, matrix) } Improved CmdStan outputs Several new output options are available for CmdStan to produce JSON files for easier processing. output save_cmdstan_config=1 will produce a JSON file which contains a serialized copy of the configuration for this run of CmdStan. This should be equivalent to what is currently printed as comments at the top of the CSV file. For sampling algorithms, the argument adaptation save_metric=1 will produce a JSON file that contains the adapted step size and inverse metric. This file can be used with the pre-existing metric argument to initialize later runs. Additionally, several bugs in the way file names wer…

Release of CmdStan 2.33
type: item
creator: rokcesnovar
date: 2023-09-05T15:52:26Z
subject: Uncategorized
description: We are very happy to announce that the 2.33.0 release of CmdStan is now available on GitHub! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. A PyStan update will follow shortly. This new release brings two new exciting features that have been in the making … Continue reading Release of CmdStan 2.33 →
encoded: We are very happy to announce that the 2.33.0 release of CmdStan is now available on GitHub! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. A PyStan update will follow shortly. This new release brings two new exciting features that have been in the making for more than a year: a new Stan language type – tuples and a new algorithm – Pathfinder. The release also features some new functions – mostly related to tuples, and other minor improvements. There are also a number of long-deprecated language features that have now become errors with this version. Contributors, sponsors and donators We would like to thank everyone who contributed to this release with their bug reports, feature or bug fix discussions, and code or code reviews. We would also like to thank all of our sponsors and donators who have supported us during this last release cycle and in years past. Thanks to the Flatiron Institute for providing us with the computing resources to run unit tests and build scripts for our repositories. You can support Stan via NumFocus or the GitHub sponsorship program. We would like to especially thank everyone who contributed to the Tuples and Pathfinder features that have been in the making for a long time (in alphabetical order): Ryan Bernstein, Steve Bronder, Bob Carpenter, Andrew Gelman, Niko Huure, Mitzi Morris, Aki Vehtari, Brian Ward and Lu Zhang! Release highlights Tuples The Stan language gained a new type: tuples! For any sequence of types, Stan provides a tuple data type. For example, tuple(real, array[5] int) xi; declares xi to be a tuple holding two values, the first of which is of type type real and the second of which is a 5-dimensional array of type int. Tuples can be of arbitrary size. The values in a tuple can be of arbitrary type, but the component types must be declared along with the declaration of the tuple. Tuples can be manipulated as a whole, or their elements may be accessed and set individuall…

Release of CmdStan 2.32
type: item
creator: rokcesnovar
date: 2023-04-21T14:42:01Z
subject: Uncategorized
description: We are very happy to announce that the 2.32.0 release of CmdStan is now available on Github! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. A PyStan update will follow shortly. This new release brings Laplace approximation, the Jacobian adjustment for the optimization algorithms, more … Continue reading Release of CmdStan 2.32 →
encoded: We are very happy to announce that the 2.32.0 release of CmdStan is now available on Github! As usual, the release of CmdStan is accompanied by new releases of Stan Math, core Stan, and Stanc3. A PyStan update will follow shortly. This new release brings Laplace approximation, the Jacobian adjustment for the optimization algorithms, more supported arguments for the eigenvalues/eigenvectors functions and a ton of bugfixes. This release also includes a number of changes in the backend that will enable some big features in the next few releases (more on that at the end of blog post). The removal of the deprecated language features that were scheduled to become errors in this version was postponed to the next release. Contributors, sponsors and donators We would like to thank everyone that contributed to this release with their bug reports, feature or bug fix discussions, and code or code reviews. We would also like to thank all of our sponsors and donators who have supported us during this last release cycle and in years past. Thanks to the Flatiron Institute for providing us with the computing resources to run unit tests and build scripts for our repositories. You can support Stan via NumFocus or the Github sponsorship program. Release highlights Laplace approximation CmdStan now supports sampling from a Laplace approximation. The new method produces a sample from a normal approximation centered at the mode of a distribution in the unconstrained space. For more see the CmdStan documentation for the laplace method. cmdstanpy/r supports should be coming in the next weeks. Jacobian adjustments for optimization Enabling the adjustment produces the maximum a posteriori (MAP) estimate in addition to the existing penalized maximum likelihood estimate. For more details see here. Miscellaneous enhancements and bugfixes eigenvalues and eigenvectors function now accept complex matrices, recursive user-defined functions no longer need forward declarations, improved Stan-to-C++ co…