Blog Archives

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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Subset replacement in pqR: Now faster and better

October 13, 2014
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Subset replacement in pqR: Now faster and better

The latest version of pqR that I just released uses a new way of implementing subset replacement operations — such as a<-1 or L$M<-v. The new approach is much faster, and eliminates some strange behaviour of the previous approach. This change affects only interpreted code. The bytecode compiler (available since R-2.13.0) introduced a different mechanism, which is

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New version of pqR with faster variable lookup, faster subset replacement, and more

October 6, 2014
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New version of pqR with faster variable lookup, faster subset replacement, and more

I’ve released a new version, pqR-2014-09-30, of my speedier, “pretty quick”, implementation of R, with some major performance improvements, and some features from recent R Core versions. It also has fixes for bugs (some also in R-3.1.1) and installation glitches. Details are in pqR NEWS. Here I’ll highlight some of the more interesting improvements. Faster variable lookup.   In both pqR

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New release of pqR, with a curated repository

June 21, 2014
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New release of pqR, with a curated repository

I have released a new version, pqR-2014-06-19, of my speedier, “pretty quick”, implementation of R.  This and the previous release (pqR-2014-02-23) are maintenance releases, with bug fixes, improved documentation, and better test procedures. The result is that pqR now works with a large collection of 3438 packages. This collection was created starting with the complete set

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Inaccurate results from microbenchmark

February 2, 2014
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Inaccurate results from microbenchmark

The microbenchmark package is a popular way of comparing the time it takes to evaluate different R expressions — perhaps more popular than the alternative of just using system.time to see how long it takes to execute a loop that evaluates an expression many times. Unfortunately, when used in the usual way, microbenchmark can give inaccurate

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