Regarding the choice of step-size parameter for NUTS, specifically at this point that “the sampler would at some iteration move to the region that needs a small step-size, and then get stuck there, as all the proposals are rejected — a problem that is easily diagnosed. Unfortunately, that’s not what will happen. Since the sampler leaves the correct distribution invariant, if it would be stuck for a very long time in some region, it must also be very unlikely to enter that region.”

Is it applicable to conventional HMC only, since there is no eventual rejection in NUTS since it is essentially a slice sampler?

]]>I am totally for the implementation of correct generalizations of operators (such as the v[-ix] that you mention in your presentation when ix has length 0).

I wonder why the R Core team is so uninterested in these improvements…

Also was happy to learn the origin of the pqR name… I thought the name was *purely* based on the alphabetical order of the letters involved :-)

]]>I plan to implement a number of other language extensions, at which point I’ll be better able to see what possible implementation or compatibility problems there are for the whole set. That might be a better time to make more formal proposals.

You can see some of these plans (which may not exactly match what I eventually implement) at http://www.cs.utoronto.ca/~radford/ftp/pqR-Rusers.pdf

]]>http://www.r-bloggers.com/get-involved-with-the-r-consortium/ ]]>

I told them about the new parser in pqR about a year ago, and offered to help with incorporating it into R Core, in a message to the r-devel list, which you can see at https://stat.ethz.ch/pipermail/r-devel/2015-September/071777.html

I received no response from R Core whatsoever.

I’ll probably post another message to r-devel in a few days, after I’ve put up a couple more blog posts on features in the new version of pqR. But obviously I have no expectation that R Core will do anything.

]]>So the question is whether you like the fact that M[1:n,1:10] is a matrix when n is greater than 1 but a simple vector when n happens to be 1. In particular, suppose you assign this value, with A <- M[1:n,1:10]. Do you like the fact that A[i,j] will then produce an error if n happened to be 1?

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