Monthly Archives: January 2013

R-package: Wilcox’ Robust Statistics updated (WRS v0.20)

January 8, 2013
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Rand Wilcox constantly updates the functions accompanying his books on robust statistics. Recently, they have been updated to version 20. The functions are available in the WRS package for R – for installation simply type install.packages("WRS", repos="http://R-Forge.R-project.org") In version 0.20, a number of functions dealing with ANCOVA have been added and some others improved. Unfortunately,

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Remembering server installation details

January 8, 2013
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Remembering server installation details

I’ve been moving part of my work to university servers, where I’m just one more peasant user with little privileges. In exchange, I can access the jobs from anywhere and I can access multiple processors if needed. Given that I have a sieve-like memory, where configuration details quickly disappear through many small holes, I’m documenting

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Speeding up R computations Pt III: parallelization

January 8, 2013
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In two previous posts, I have written about how you can speed up your R computations either by using strange notation and non-standard functions or by compiling your code. Last year my department bought a 64-core computational server, which allowed me ...

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Open Science Challenge

January 8, 2013
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Open Science Challenge

Open Science Science is becoming more open in many areas: publishing, data sharing, lab notebooks, and software. There are many benefits to open science. For example, sharing research data alongside your publications leads to increased citation ra...

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Handling Strings with Rcpp

January 8, 2013
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This is a quick example of how you might use Rcpp to send and receive R ‘strings’ to and from R. We’ll demonstrate this with a few operations. Sort a String with R Note that we can do this in R in a fairly fast way: my_strings <-...

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Using Rcout for output synchronised with R

January 8, 2013
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The Writing R Extensions manual, which provides the gold standard of documentation as far as extending R goes, suggests to use Rprintf and REprintf for output (from C/C++ code) as these are matched to the usual output and error streams maintained by R ...

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Open Science Challenge

January 8, 2013
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Open Science Challenge

Open Science Science is becoming more open in many areas: publishing, data sharing, lab notebooks, and software. There are many benefits to open science. For example, sharing research data alongside your publications leads to increased citation ra...

Read more »

Reserving based on log-incremental payments in R, part I

January 8, 2013
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A recent post on the PirateGrunt blog on claims reserving inspired me to look into the paper Regression models based on log-incremental payments by Stavros Christofides , published as part of the Claims Reserving Manual (Version 2) of the Institute of Actuaries.The paper is available together with a spread sheet model, illustrating the calculations. It...

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Improving Twitter Search with Real-Time Human Computation

January 7, 2013
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(This is a post from the Twitter Engineering Blog that I wrote with Alpa Jain.) One of the magical things about Twitter is that it opens a window to the world in real-time. An event happens, and just seconds later, it’s shared for people across the planet to see. Consider, for example, what happened when...

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The myth of the missing Data Scientist

January 7, 2013
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The myth of the missing Data Scientist

Much has been said about the dire shortage of Data Scientists looming on the horizon. With the spectre of Big …Continue reading »

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