(Py, R, Cmd) Stan 2.3 Released

June 26, 2014

(This article was first published on Statistical Modeling, Causal Inference, and Social Science » R, and kindly contributed to R-bloggers)

We’re happy to announce RStan, PyStan and CmdStan 2.3.
Instructions on how to install at:

As always, let us know if you’re having problems or have comments or suggestions.

We’re hoping to roll out the next release a bit quicker this time, because we have lots of good new features that are almost ready to go (vectorizing multivariate normal, higher-order autodiff for probability functions, differential equation solver, L-BFGS optimizer).

Here are the official release notes.

v.2.3.0 (18 June 2014)

We had a record number of user-submitted patches this go around.
Thanks to everyone!

New Features
* user-defined function definitions added to Stan language
* cholesky_corr data type for Cholesky factors of correlation matrices
* a^b syntax for pow(a,b)  (thanks to Mitzi Morris)
* reshaping functions: to_matrix(), to_vector(), to_row_vector(), 
  to_array_1d(), to_array_2d()
* matrix operations: quad_form_sym() (x' *Sigma * x), QR decompositions
  qr_Q(), qr_R()
* densities: Gaussian processes multi_gp_log(), multi_gp(), 
  and alternative negative binomial parameterization neg_binomial_2()
* random number generator: multi_normal_cholesky_rng()
* sorting: sort_indices_*() for returning indexes in sorted order by
* added JSON parser to C++ (not exposed through interfaces yet; thanks
  to Mitzi Morris)
* many fixes to I/O for data and inits to check consistency and
  report errors
* removed some uses of std::cout where they don't belong
* updated parser for C++11 compatibility (thanks to Rob Goedman)

New Developer
* added Marco Inacio as core developer

* turned off Eigen asserts
* efficiency improvements to posterior analysis print

* Clarified licensing policy for individual code contributions
* Huge numbers of fixes to the documentation, including many
  user-contributed patches (thanks!), fixes to parallelization in
  CmdStan, Windows install instructions, boundaries for Dirichlet and
  Beta, removed suggestion to use floor and ceiling as indices,
  vectorized many models, clarified that && doesn't short circuit,
  clarified von Mises normalization, updated censoring doc (thanks
  to Alexey Stukalov), negative binomial doc enhanced, new references,
  new discussion of hierarchical models referencing Betancourt and
  Girolami paper, 
* Avraham Adler was particularly useful in pointing out and fixing
  documentation errors

Bug Fixes
* fixed bug in lkj density
* fixed bug in Jacobian for corr_matrix data type
* fix cholesky_cov_matrix test code to allow use as parameter
* fixed poisson_rng, neg_binomial_rng
* allow binary operations (e.g., < and >) within range constraints
* support MS Visual Studio 2008
* fixed memory leaks in categorical sampling statement, categorical_log
  function, and softmax functions
* removed many compiler warnings
* numerous bug fixes to arithmetic test code conditions and messages,
  including calls from 
* fixed model crashes when no parameter specified
* fixed template name conflicts for some compiler bugs (thanks Kevin
  S. Van Horn)

Code Reorganizations & Updates
* CmdStan is now in its own repository on GitHub: stan-dev/cmdstan
* consolidate and simplify error handling across modules
* pulled functionality from CmdStan command class and PyStan and RStan
  into Stan C++
* generalized some interfaces to allow std::vector as well as Eigen
  for compatibility
* generalize some I/O CSV writer capabilities
* optimization output text cleaned up
* integer overflow during I/O now raises informative error messages
* const correctness for chains (thanks Kevin S. Van Horn)

The post (Py, R, Cmd) Stan 2.3 Released appeared first on Statistical Modeling, Causal Inference, and Social Science.

To leave a comment for the author, please follow the link and comment on their blog: Statistical Modeling, Causal Inference, and Social Science » R.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)