Blog Archives

parallelsugar: An implementation of mclapply for Windows

October 14, 2015
By

An easy way to run R code in parallel on a multicore system is with the mclapply() function. Unfortunately, mclapply() does not work on Windows machines because the mclapply() implementation relies on forking and Windows does not support forking. Previously, I published a hackish solution that implemented a fake mclapply() for Windows users with one

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rstanmulticore: A cross-platform R package to automatically run RStan MCMC chains in parallel

May 1, 2015
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*** This work has been supported by a grant from the Spencer Foundation (#201400002). The views expressed are those of the author and do not necessarily reflect those of the Spencer Foundation. *** It seems that the heir to WinBUGS is Stan. With Stan, reasonably complex Bayesian models can be expressed in a compact way

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rstanmulticore: A cross-platform R package to automatically run RStan MCMC chains in parallel

May 1, 2015
By

*** This work has been supported by a grant from the Spencer Foundation (#201400002). The views expressed are those of the author and do not necessarily reflect those of the Spencer Foundation. *** It seems that the heir to WinBUGS is Stan. With Stan, reasonably complex Bayesian models can be expressed in a compact way

Read more »

Implementing mclapply() on Windows: a primer on embarrassingly parallel computation on multicore systems with R

July 14, 2014
By

An easy way to run R code in parallel on a multicore system is with the mclapply() function. Unfortunately, mclapply() does not work on Windows machines because the mclapply() implementation relies on forking and Windows does not support forking. For me, this is somewhat of a headache because I am used to using mclapply(), and

Read more »

Implementing mclapply() on Windows: a primer on embarrassingly parallel computation on multicore systems with R

July 14, 2014
By

An easy way to run R code in parallel on a multicore system is with the mclapply() function. Unfortunately, mclapply() does not work on Windows machines because the mclapply() implementation relies on forking and Windows does not support forking. For me, this is somewhat of a headache because I am used to using mclapply(), and

Read more »

Writing a Metropolis-Hastings within Gibbs sampler in R for a 2PL IRT model (9 posts)

November 5, 2013
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Last year, Brian Junker, Richard Patz, and I wrote an invited chapter for the (soon to be released) update of the classic text Handbook of Modern Item Response Theory (1996). The chapter itself is meant to be an update of the classic IRT in MCMC papers Patz & Junker (1999a, 1999b). To support the chapter,

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Writing a Metropolis-Hastings within Gibbs sampler in R for a 2PL IRT model (9 posts)

November 5, 2013
By

Last year, Brian Junker, Richard Patz, and I wrote an invited chapter for the (soon to be released) update of the classic text Handbook of Modern Item Response Theory (1996). The chapter is meant to be an update of the … Continue reading →

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For faster R use OpenBLAS instead: better than ATLAS, trivial to switch to on Ubuntu

July 9, 2013
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R speeds up when the Basic Linear Algebra System (BLAS) it uses is well tuned. The reference BLAS that comes with R and Ubuntu isn’t very fast. On my machine, it takes 9 minutes to run a well known R … Continue reading →

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For faster R use OpenBLAS instead: better than ATLAS, trivial to switch to on Ubuntu

July 9, 2013
By

R speeds up when the Basic Linear Algebra System (BLAS) it uses is well tuned. The reference BLAS that comes with R and Ubuntu isn't very fast. On my machine, it takes 9 minutes to run a well known R benchmarking script. If I use ATLAS, an optimized BLAS that can be easily installed, the

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My Stat Bytes talk, with slides and code

June 24, 2013
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On Thursday of last week I gave a short informal talk to Stat Bytes, the CMU Statistics department‘s twice a month computing seminar. Quick tricks for faster R code: Profiling to Parallelism Abstract: I will present a grab bag of … Continue reading →

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