CmdStan & Stan 2.40 Release Candidates

General
1

I am happy to announce that the latest release candidates of CmdStan and Stan are now available on Github!

This release cycle brings the embedded laplace approximation, new functions, and many fixes.

You can find the release candidate for CmdStan here. Instructions for installing are given at the bottom of this post.

Please test the release candidate with your models and report if you experience any problems. We also kindly invite you to test the new features and provide feedback. If you feel some of the new features could be improved or should be changed before the release, please do not hesitate to comment.

The Stan development team appreciates your time and help in making Stan better.

If everything goes according to plan, the 2.40 version will be released next week.

Below are some of the highlights of the new release.

New options for 1d integration

Several new integration functions have been added:

  • integrate_1d_double_exponential, a variadic version of the existing 1-d integrator
  • integrate_1d_gauss_kronrod, a variadic function with a different quadrature rule

Preliminary documentation can be found here.

Matrix reshaping functions

New functions to_vector_array(matrix) -> array[] vector to_row_vector_array(matrix) -> array[] row_vector, as well as new overloads for to_matrix for arrays of (row-)vectors.

New poisson_binomial distribution

A new discrete PMF, CDF, CCDF, and RNG functions available.

Preliminary documentation is available here

Other changes

There have been various other bug fixes and usability improvements this release, thank you to everyone who reported a bug in the last few months!

The same .stanfunctions can be #included multiple times without leading to errors about duplicated function definitions.

Updated the Math library to use Eigen 5.0.1

The vectorized RNGs were updated to fix a crash when given size-0 inputs.

The compiler has fewer dependencies, with a smaller binary size and improved speeds.

More details on all of the above and more are available in the preliminary release notes

How to install?

Download the tar.gz file from the link above, extract it and use it the way you use any Cmdstan release. We also have an online Cmdstan guide available at CmdStan User’s Guide .

If you are using cmdstanpy you can install the release candidate using


cmdstanpy.install_cmdstan(version='2.40.0-rc1')

With CmdStanR you can install the release candidate using


cmdstanr::install_cmdstan(version = "2.40.0-rc1", cores = 4)
2

  1. I tested compiling a bunch of Stan files which use the new integrate and laplace functions, and all did compile.

  2. --Oexperimental fails with empty loops, e.g.

parameters { real y; }
model {
  real x = 0;
  for (i in 1:0) x += 1;
  y ~ std_normal();
}
  1. --Oexperimental fails with posteriordb model state_wide_presidential_votes-hierarchical_gp.stan. stanc accepts it, then g++ rejects the generated code:
stan::model::assign(lcm_sym82__, multiply(multiply(17, prop_var), tot_var),
                    "assigning variable lcm_sym82__");
→ candidate expects 4 arguments, 3 provided
3

Thanks Aki!

#2 has an easy fix and I’ve put up a PR. #3 actually looks like an older issue, since I can recreate it on 2.39. Could be Issue · GitHub

4

Not sure in which repo this should be fixed (cmdstanr or cmdstan), so I didn’t create an issue yet (and yesterday it was just too late that I didn’t have time to create issues, and also why I didn’t check the older version for #3).

  • stanc takes one optimization level and the last flag wins. For example, --Oexperimental --O1 is byte-identical to O1 output. cmdstanr composes <include-paths> <your stanc_options> <make/local STANCFLAGS>, and if make/local has STANCFLAGS=--O1, then stanc_options = list("Oexperimental") is silently downgraded