Blog Archives

“An R package” or “A R package”

March 26, 2011
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“An R package” or “A R package”

I’m currently writing some lecture notes on R and I used the phrase “a R package” without thinking. Since the word following the article “a” was a consonant, I automatically went for “a” instead of “an”. The problem is that “R” sounds likes a vowel, so “a R package” grates on the listener. The correct

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Graphical Display of R Package Dependencies

March 23, 2011
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Graphical Display of R Package Dependencies

In some work that I am currently involved in, we have to decide which GUI engine we should use. As an obvious starter, we decided to have a look at what other people are using in their packages. While cran helpfully displays all the R packages that are available, it doesn’t (I don’t think), give

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R programming books (updated)

January 28, 2011
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R programming books (updated)

In a recent post, I asked for suggestions for introductory R computing books. In particular, I was looking for books that: Assume no prior knowledge of programming. Assume very little knowledge of statistics. For example, no regression. Are cheap, since they are for undergraduate students. Some of my cons aren’t really downsides as such. Rather,

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CPU and GPU trends over time

January 25, 2011
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CPU and GPU trends over time

GPUs seem to be all the rage these days. At the last Bayesian Valencia meeting, Chris Holmes gave a nice talk on how GPUs could be leveraged for statistical computing. Recently Christian Robert arXived a paper with parallel computing firmly in mind. In two weeks time I’m giving an internal seminar on using GPUs for

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Parsing and plotting time series data

January 15, 2011
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Parsing and plotting time series data

This morning I came across a post which discusses the differences between scala, ruby and python when trying to analyse time series data. Essentially, there is a text file consisting of times in the format HH:MM and we want to get an idea of its distribution. Tom discusses how this would be a bit clunky

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Statistical podcast: Random and Pseudorandom

January 14, 2011
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Statistical podcast: Random and Pseudorandom

This morning when I downloaded the latest version of In our time, I was pleased to see that this weeks topic was “Random and Peudorandom.” If you’re not familiar with “In our time”, then I can I definitely recommend the series. Each week three academics and Melvyn Bragg discuss a particular topic from history, science,

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Survival paper (update)

January 13, 2011
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Survival paper (update)

In a recent post, I discussed some  statistical consultancy I was involved with. I was quite proud of the nice ggplot2 graphics I had created. The graphs nicely summarised the main points of the paper: I’ve just had the proofs from the journal, and next to the graphs there is the following note: It is

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Random variable generation (Pt 3 of 3)

January 12, 2011
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Random variable generation (Pt 3 of 3)

Ratio-of-uniforms This post is based on chapter 1.4.3 of Advanced Markov Chain Monte Carlo.  Previous posts on this book can be found via the  AMCMC tag. The ratio-of-uniforms was initially developed by Kinderman and Monahan (1977) and can be used for generating random numbers from many standard distributions. Essentially we transform the random variable of

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R programming books

December 21, 2010
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R programming books

My sabbatical is rapidly coming to an end, and I have to start thinking more and more about teaching. Glancing over my module description for the introductory computational statistics course I teach, I noticed that it’s a bit light on recommend/background reading. In fact it has only two books: A first course in statistical programming

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Logical operators in R

December 14, 2010
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Logical operators in R

In R, the operators “|” and “&” indicate the logical operations OR and AND. For example, to test if x equals 1 and y equals 2 we do the following: > x = 1; y = 2 > (x == 1) & (y == 2) TRUE However, if you are used to programming in

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