NIMBLE has a post-doc or software developer position open

The NIMBLE statistical software project at the University of California, Berkeley is looking for a post-doc or statistical software developer. NIMBLE is a tool for writing hierarchical statistical models and algorithms from R, with compilation via code-generated C++. Major methods currently include MCMC and sequential Monte Carlo, which users can customize and extend. More information can be found at https://R-nimble.org. Currently we seek someone with experience in computational statistical methods such as MCMC and excellent software development skills in R and C++. This could be someone with a Ph.D. in Statistics, Computer Science, or an applied statistical field in which they have done relevant work. Alternatively it could be someone with relevant experience in computational statistics and software engineering. The scope of work can include both core development of NIMBLE and development and application of innovative methods using NIMBLE, with specific focus depending on the background of the successful candidate. Applicants must have either a Ph.D. in a relevant field or have a proven record of relevant work. Please send cover letter, CV, and the names and contact information for three references to nimble.stats@gmail.com. Applications will be considered on a rolling basis starting 30 January, 2018.

Version 0.6-8 of NIMBLE released

We’ve released the newest version of NIMBLE on CRAN and on our website a week ago. Version 0.6-8 has a few new features, and more are on the way in the next few months.

New features include:

  • the proper Gaussian CAR (conditional autoregressive) model can now be used in BUGS code as dcar_proper, which behaves similarly to BUGS’ car.proper distribution;
  • a new nimbleMCMC function that provides one-line invocation of NIMBLE’s MCMC engine, akin to usage of JAGS and WinBUGS through R;
  • a new runCrossValidate function that will conduct k-fold cross-validation of NIMBLE models fit by MCMC;
  • dynamic indexing in BUGS code is now allowed by default;
  • and a variety of bug fixes and efficiency improvements.

Please see the NEWS file in the installed package for more details.

Version 0.6-6 of NIMBLE released!

We’ve just released the newest version of NIMBLE on CRAN and on our website. Version 0.6-6 has some important new features, and more are on the way in the next few months.

New features include:

  • dynamic indexes are now allowed in BUGS code — indexes of a variable no longer need to be constants but can be other nodes or functions of other nodes; for this release this is a beta feature that needs to be enabled with nimbleOptions(allowDynamicIndexing = TRUE);
  • the intrinsic Gaussian CAR (conditional autoregressive) model can now be used in BUGS code as dcar_normal, which behaves similarly to BUGS’ car.normal distribution;
  • optim is now part of the NIMBLE language and can be used in nimbleFunctions;
  • it is possible to call out to external compiled code or back to R functions from a nimbleFunction using nimbleExternalCall() and nimbleRcall() (this is an experimental feature);
  • the WAIC model selection criterion can be calculated using the calculateWAIC() method for MCMC objects;
  • the bootstrap and auxiliary particle filters can now return their ESS values;
  • and a variety of bug fixes.

Please see the NEWS file in the installed package for more details.

Finally, we’re deep in the midst of development work to enable automatic differentiation, Tensorflow as an alternative back-end computational engine, additional spatial models, and Bayesian nonparametrics.

 

Version 0.6-5 of NIMBLE released!

We’ve just released the newest version of NIMBLE on CRAN and on our website. Version 0.6-5 is mostly devoted to bug fixes and packaging fixes for CRAN, but there is some new functionality:

  • addition of the functions  c(), seq(), rep(), `:`, diag() for use in BUGS code;
  • addition of two improper distributions (dflat and dhalfflat) as well as the inverse-Wishart distribution;
  • the ability to estimate the asymptotic covariance of the estimates in NIMBLE’s MCEM algorithm;
  • the ability to use nimbleLists in any nimbleFunction, newly including nimbleFunctions without setup code;
  • and a variety of bug fixes and better error trapping.

Please see the NEWS file in the installed package for more details.

Version 0.6-4 of NIMBLE released!

We’ve just released the newest version of NIMBLE on CRAN and on our website. Version 0.6-4 has a bunch of new functionality for writing your own algorithms (using a natural R-like syntax) that can operate on user-provided models, specified using BUGS syntax. It also enhances the functionality of our built-in MCMC and other algorithms.

  • addition of the functions  c(), seq(), rep(), `:`, diag(), dim(), and which() for use in the NIMBLE language (i.e., run code) — usage generally mimics usage in R;
  • a complete reorganization of the User Manual, with the goal of clarifying how one can write nimbleFunctions to program with models;
  • addition of the adaptive factor slice sampler, which can improve MCMC sampling for correlated blocks of parameters;
  • addition of a new sampler that can handle non-conjugate Dirichlet settings;
  • addition of a nimbleList data structure that behaves like R lists for use in nimbleFunctions;
  • addition of eigendecomposition and SVD functions for use in the NIMBLE language;
  • additional flexibility in providing initial values for numeric(), logical(), integer(), matrix(), and array();
  • logical vectors and operators can now be used in the NIMBLE language;
  • indexing of vectors and matrices can now use arbitrary numeric and logical vectors;
  • one can now index a vector of node names provided to values(), and more general indexing of node names in calculate(), simulate(), calculateDiff() and getLogProb();
  • addition of the inverse-gamma distribution;
  • use of recycling for distribution functions used in the NIMBLE language;
  • enhanced MCMC configuration functionality;
  • users can specify a user-defined BUGS distribution by simply providing a user-defined ‘d’ function without an ‘r’ function for use when an algorithm doesn’t need the ‘r’ function;
  • and a variety of bug fixes, speedups, and better error trapping and checking.

Please see the NEWS file in the installed package for more details.

NIMBLE is hiring a programmer

This position includes work to harness parallel processing and automatic differentiation, to generate interfaces with other languages such as Python, to improve NIMBLE’s scope and efficiency for large statistical models, and to build other new features into NIMBLE.

The work will involve programming in R and C++, primarily designing and implementing software involving automated generation of C++ code for class and function definitions, parallel computing, use of external libraries for automatic differentiation and linear algebra, statistical algorithms and related problems. The position will also involve writing documentation and following good open-source software practices.

See here to apply.

Version 0.6-3 released.

Version 0.6-3 is a very minor release primarily intended to address some CRAN packaging issues that do not affect users. We also fixed a bug involving MCEM functionality and a bug that prevented use of the sd() and var() functions in BUGS code.

For most users, there is probably no need to upgrade from version 0.6-2.

Version 0.6-2 released!

Version 0.6-2 is a minor release with a variety of useful functionality for users.

Changes as of Version 0.6-2 include:

  • user-defined distributions can be used in BUGS code without needing to call the registerDistributions() function (unless one wants to specify alternative parameterizations, distribution range or that the distribution is discrete),
  • users can now specify the use of conjugate (Gibbs) samplers for nodes in a model,
  • NIMBLE will now check the run code of nimbleFunctions for functions (in particular R functions) that are not part of the DSL and will not compile,
  • added getBound() functionality to find the lower and upper bounds of a node either from R or in DSL code,
  • added functionality to get distributional information about a node in a model or information about a distribution based on the name of the density function; these may be useful in setup code for algorithms,
  • multinomial and categorical distributions now allow ‘probs’ arguments that do not sum to one (these will be internally normalized) and
  • a variety of bug fixes.

Please see the NEWS file in the installed package for more details.

Version 0.5-1 of NIMBLE released!

Version 0.5-1 is officially a minor release, but it actually has quite a bit in it, in particular the addition/improvement of a number of our algorithms. In addition there are some more improvements in our speed in building and compiling models and algorithms.

Changes as of Version 0.5-1 include:

  • the addition of a variety of sequential Monte Carlo (aka particle filtering) algorithms, including particle MCMC samplers for use within an MCMC,
  • a greatly improved MCEM algorithm with an automated convergence and stopping criterion,
  • new syntax for declaring multivariate variables in the NIMBLE DSL, namely numeric(), integer(), matrix(), and array(), with declare() now deprecated,
  • addition of the multivariate-t distribution for use in BUGS and DSL code,
  • a new binary MCMC sampler for discrete 0/1 nodes,
  • addition of functionality to our random walk sampler to allow sampling on the log scale and use of reflection,
  • more flexible use of forwardsolve(), backsolve(), and solve(), including use in BUGS code, and
  • a variety of other items.

Please see the NEWS file in the source package.

Version 0.5 released!

We’ve just released the next major version of NIMBLE.

Changes include

  • more efficient computations for conjugate sampling,
  • additional automated checking of BUGS syntax to improve NIMBLE’s warning/error messages,
  • new API functionality to allow the use of syntax such as model$calculate(), etc. (syntax such as calculate(model) still works),
  • new API functionality for MCMC sampler specification,
  • improvements in speed and memory use in building models,
  • addition of forwardsolve, backsolve, and solve to the NIMBLE DSL, and
  • a variety of other items.

More details in the NEWS file that accompanies the package.

We anticipate being on CRAN in coming weeks and a next release soon that will include a full suite of sequential Monte Carlo (i.e., particle filtering) algorithms.