Posts filed under ‘Machine Learning’

New version of pqR, with automatic differentiation and arithmetic on lists

I’ve released pqR-2020-07-23, a new version of my variant implementation of R.  You can install it on Linux, Windows, or Mac as described at Installation must currently be from source, similarly to source installs of R Core versions of R.

This version has preliminary implementations of automatic differentiation and of arithmetic on lists. These are both useful for gradient-based optimization, such as maximum likelihood estimation and neural network training, as well as gradient-based MCMC methods. List arithmetic is helpful when dealing with models that have several groups of parameters, which are most conveniently represented using a list of vectors or matrices, rather than a single vector.

You can read the documentation on these facilities here and here. Some example programs are in this repository. I previously posted about the automatic differentiation facilities here. Automatic differentiation and arithmetic on lists for pqR are both discussed in this talk, along with some other proposals.

For the paranoid, here are the shasum values for the compressed and uncompressed tar files that you can download from, allowing you to verify that they were downloaded uncorrupted:

c1b389861f0388b90122cbe1038045da30879785 pqR-2020-07-23.tar.gz
04b4586601d8796b12c310cd4bf81dc057f33bb2 pqR-2020-07-23.tar

2020-07-25 at 1:38 pm Leave a comment

Software for Flexible Bayesian Modeling – New release

I’ve released a new version of my Software for Flexible Bayesian Modeling and Markov Chain Sampling (FBM). This is the first public release since 2004, with the first release of the precursor software being in 1995. There was a version mostly completed in 2007 that never got released (due to my not getting around to checking that I’d fixed up everything). The new version has the changes from 2007 plus some more recent updates, including new features used for the tests in this paper.

FBM implements several general-purpose Markov chain sampling methods, such as Metropolis updates, Hamiltonian (Hybrid) Monte Carlo, and slice sampling. These methods can be applied to distributions defined by simple formulas, including posterior distributions for simple Bayesian models. Several more specialized modules have been written that implement posterior distributions for more complex models, including Bayesian neural networks, Gaussian process models, and mixture models (including Dirichlet process mixture models).

2020-02-17 at 4:06 pm 2 comments

Automatic differentiation in pqR

I’ve released a version of my pqR implementation of R that has extensions for automatic differentiation. This is not a stable release, but it can be downloaded from — look for the test version at the bottom — and installed the same as other pqR versions (from source, so you’ll need C and Fortran compilers).

Note that this version very likely has various bugs — mostly showing up only if you use automatic differentiation, I hope.

You can read about the automatic differentation facilities here, or with help(Gradient) after installing the test version. Below are a few examples to show a bit of what you can do.

2019-07-06 at 5:39 pm 1 comment


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