Blog Archives

Faster garbage collection in pqR

November 29, 2018
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Faster garbage collection in pqR

The latest version of pqR and the version before as well use a new garbage collector, and new memory layouts for R objects, which both reduce memory usage and considerably speed up garbage collection. Here, I’ll give an overview of how the new scheme works, and present some performance comparisons with R-3.5.1.  Some more details are presented

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The new pqR parser, and R’s “else” problem

November 27, 2018
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The new pqR parser, and R’s “else” problem

The latest version of pqR (mostly) solves R’s “else” problem, by modifying the new parser previously introduced in pqR.  I’ll explain the “else” problem and solution here, and also present other advantages of pqR’s parser over the R Core parser, including a big speed advantage in one context. The “else” problem in R Probably most

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New version of pqR, with major speed improvements

November 25, 2018
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New version of pqR, with major speed improvements

I’ve released pqR-2018-11-18, a new version of my variant implementation of R.  You can install it on Linux, Windows, or Mac as described at pqR-project.org. Installation must currently be from source, similarly to source installs of R Core versions of R. This version has some major speed improvements, as well as some new features. I’ll details

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New release of pqR — faster, and now compatible with R-2.15.1

October 8, 2016
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New release of pqR — faster, and now compatible with R-2.15.1

I have released a new version of my “pretty quick” R interpreter, pqR-2016-10-05. One major change with this version is that pqR, which was based on R-2.15.0, is now compatible with R-2.15.1.  This allows for an increased number of packages in the pqR repository. This release also has some significant speed improvements, a new form of the “for” statement, for

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Fixing R’s design flaws in a new version of pqR

June 25, 2016
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Fixing R’s design flaws in a new version of pqR

I’ve released a new version of my pqR implementation of R. This version introduces extensions to the R language that fix some long-standing design flaws that were inherited from S. These language extensions make it easier to write reliable programs, that work even in edge cases, such as data sets with one observation. In particular, the extensions fix the problems

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Critique of ‘Debunking the climate hiatus’, by Rajaratnam, Romano, Tsiang, and Diffenbaugh

January 10, 2016
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Critique of ‘Debunking the climate hiatus’,  by Rajaratnam, Romano, Tsiang, and Diffenbaugh

Records of global temperatures over the last few decades figure prominently in the debate over the climate effects of CO2 emitted by burning fossil fuels, as I discussed in my first post in this series, on What can global temperature data tell us? One recent controversy has been whether or not there has been a `pause’

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Has there been a ‘pause’ in global warming?

December 19, 2015
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Has there been a ‘pause’ in global warming?

As I discussed in my previous post, records of global temperatures over the last few decades figure prominently in the debate over the climate effects of CO2 emitted by burning fossil fuels. I am interested in what this data says about which of the reasonable positions in this debate is more likely to be true —  the

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What can global temperature data tell us?

December 3, 2015
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What can global temperature data tell us?

Debates about anthropogenic climate change often centre around data on changes in global temperatures over the last few decades. There are good scientific reasons to look at this data, but it also plays a prominent role in political advocacy, sometimes fairly, sometimes not so fairly. This is the first in a series of posts in

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Exact computation of sums and means

May 21, 2015
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Exact computation of sums and means

A while ago, I came across a mention of the Python math.fsum function, which sums a set of floating-point values exactly, then rounds to the closest floating point value. This seemed useful. In particular, I thought that if it’s fast enough it could be used instead of R’s rather primitive two-pass approach to trying to compute

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How large vectors in R might be stored compactly

April 30, 2015
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How large vectors in R might be stored compactly

Vectors in R can currently have elements of two sizes — 8-byte double-precision floating-point elements for `numeric’ vectors, or 4-byte elements for `integer’ or `logical’ vectors.  You can also have vectors whose elements are 1-byte `raw’ values, but these raw vectors don’t support negative numbers, or NA values, so they aren’t suitable for general use. It seems that lots of

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