The new Stan 1.1.1, featuring Gaussian processes!

February 6, 2013

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

Screen Shot 2013-02-05 at 9.02.35 PM

We just released Stan 1.1.1 and RStan 1.1.1

As usual, you can find download and install instructions at:

This is a patch release and is fully backward compatible with Stan and RStan 1.1.0. The main thing you should notice is that the multivariate models should be much faster and all the bugs reported for 1.1.0 have been fixed. We’ve also added a bit more functionality. The substantial changes are listed in the following release notes.

v1.1.1 (5 February 2012)

Bug Fixes
* fixed bug in comparison operators, which swapped operator< with operator<= and swapped operator> with operator>= semantics
* auto-initialize all variables to prevent segfaults
* atan2 gradient propagation fixed
* fixed off-by-one in NUTS treedepth bound so NUTS goes at most to specified tree depth rather than specified depth + 1
* various compiler compatibility and minor consistency issues
* fixed bug in metaprogram preventing lower/upper bound constraints on matrices
* fixed print error for number of kept samples
* fixed floating point literal precision issue in code generation
* fixed bug in bernoulli_log for boundary chance of success theta=0 or theta=1
* many doc patches (mostly due to user comments — thanks!)
* replace boost sign() to avoid compiler conflicts
* trapping mismatched dimension assignments in arrays, matrices, and vectors

* user’s guide chapters w. sample models
+ gaussian processes
+ measurement error and meta-analysis
+ clustering (soft k-means, LDA, naive Bayes)
+ ARCH, GARCH model section in regression chapter
* sample models
+ hidden Markov models (HMMs)
+ non-negative matrix factorization (NNMF)
* speed improvements to multivariate models and matrix solvers
+ mdivide_left, mdivide_left_tri_low, mdivide_right, mdivide_right_tri_low
+ determinant, log_determinant
+ inverse
* much more extensive probability tests
* unstacked vari for multivariate auto-diff unfolding
* faster multiply self transpose / columns_dot_self
* cleaned up error messages for size mismatches in accessors
* simplified vector view expression template parameterization
* cleaned up many –pedantic compiler warnings

New Functions
* log absolute determinant, with optimized gradients
* probability functions
+ multivariate normal, precision parameterization
* model timing and n_eff output in CSV for all test models
* ongoing vectorizations and reparameterization of probability functions
* faster Phi_approx computing an approximate cumulative unit normal density
* added dims() function to extract dimensions of arrays of scalars, vectors, and matrices
* added size() function to extract the number of elements in an array

– The Stan Development Team

As usual, we thank the Department of Energy, Institute of Education Sciences, and National Science Foundation for partial support of this work. Your tax dollars are contributing to the public good that is open algorithms and open-source software.

The post The new Stan 1.1.1, featuring Gaussian processes! 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. 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.


Mango solutions

RStudio homepage

Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training


CRC R books series

Contact us if you wish to help support R-bloggers, and place your banner here.

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)