Posts Tagged ‘ R ’

R and Data Mining Workshop at AusDM 2014, Brisbane, 27 November

November 24, 2014
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R and Data Mining Workshop at AusDM 2014, Brisbane, 27 November

R and Data Mining Workshop at AusDM 2014 http://ausdm14.ausdm.org/workshop There will be a half-day workshop on R and Data Mining at the AusDM 2014 conference in Brisbane, Thursday afternoon, 27 November. The workshop will be composed of several sessions on … Continue reading →

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GTrendsR package to Explore Google trending for Field Dependent Terms

November 24, 2014
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GTrendsR package to Explore Google trending for Field Dependent Terms

My friend, Steve Simpson, introduced me to Philippe Massicotte and Dirk Eddelbuettel’s GTrendsR GitHub package this week. It’s a pretty nifty wrapper to the Google Trends API that enables one to search phrase trends over time. The trend indices that … Continue reading →

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an ABC experiment

November 23, 2014
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an ABC experiment

  In a cross-validated forum exchange, I used the code below to illustrate the working of an ABC algorithm: Hence I used the median and the mad as my summary statistics. And the outcome is rather surprising, for two reasons: the first one is that the posterior on the mean μ is much wider than […]

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Statistics for Big Data

November 22, 2014
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Statistics for Big Data

Doctoral programme in cloud computing for big data I’ve spent much of this year working to establish our new EPSRC Centre for Doctoral Training in Cloud Computing for Big Data, which partly explains the lack of posts on this blog in recent months. The CDT is now established, with 11 students in the first cohort, […]

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Free Stanford online course on Statistical Learning (with R) starting on 19 Jan 2015

November 21, 2014
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Free Stanford online course on Statistical Learning (with R) starting on 19 Jan 2015

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and … Continue reading →

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Visualization of probabilistic forecasts

November 21, 2014
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Visualization of probabilistic forecasts

This week my research group discussed Adrian Raftery’s recent paper on “Use and Communication of Probabilistic Forecasts” which provides a fascinating but brief survey of some of his work on modelling and communicating uncertain futures. Coincidentally, today I was also sent a copy of David Spiegelhalter’s paper on “Visualizing Uncertainty About the Future”. Both are […]

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RNA-seq Data Analysis Course Materials

November 20, 2014
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RNA-seq Data Analysis Course Materials

Last week I ran a one-day workshop on RNA-seq data analysis in the UVA Health Sciences Library. I set up an AWS public EC2 image with all the necessary software installed. Participants logged into AWS, launched the image, and we kicked off the morning ...

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rmarkdown: Alter Action Depending on Document

November 18, 2014
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rmarkdown: Alter Action Depending on Document

Can I see a show of hands for those who love rmarkdown? Yeah me too. One nifty feature is the ability to specify various document prettifications in the YAML of a .Rmd document and then use: The Problem Have you … Continue reading →

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First steps with ChainLadder: Import triangle from Excel into R

November 18, 2014
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First steps with ChainLadder: Import triangle from Excel into R

Taking the first step is often the hardest: getting data from Excel into R. Suppose you would like to use the ChainLadder package to forecast future claims payments for a run-off triangle that you have stored in Excel.How do you get the triangle into R...

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Le Monde puzzle [#887bis]

November 15, 2014
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Le Monde puzzle [#887bis]

As mentioned in the previous post, an alternative consists in finding the permutation of {1,…,N} by “adding” squares left and right until the permutation is complete or no solution is available. While this sounds like the dual of the initial solution, it brings a considerable improvement in computing time, as shown below. I thus redefined […]

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