1962 search results for "twitteR"

socialR: Reproducible Research & Notebook integration with R

December 10, 2010
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socialR: Reproducible Research & Notebook integration with R

I’ve created an R package that uses social media tools for reproducible research.  The goal of the package is this: whenever I run a code, output figures are automatically added to my figure repository (Flickr), linked to the timestamped version of the code that produced them in the code repository.  Figures should be tagged by

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Confidence bands with lattice and R

December 10, 2010
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Confidence bands with lattice and R

If you use lattice with R, and you need to plot confidence limits in your graphic, then panel.smoother and panel.quantile from latticeExtra will help you with this task. These functions internally calculate the error bounds and use panel.polygon from lattice. If you need to plot your own confidence limits, then you have to define a

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An R interface to the Google Prediction API

December 10, 2010
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An the New York R User Group* last night, 100 R users heard Ni Wang and Max Lin talk explain how "R is one of the important tools used by analysts and engineers at Google for analyzing data". During the talk, Lin revealed that Google plans to make "R more integrated with internal machine learning algorithms and infrastructure", and...

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An R interface to the Google Prediction API

December 10, 2010
By

An the New York R User Group* last night, 100 R users heard Ni Wang and Max Lin talk explain how "R is one of the important tools used by analysts and engineers at Google for analyzing data". During the talk, Lin revealed that Google plans to make "R more integrated with internal machine learning algorithms and infrastructure", and...

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Truly random [again]

December 9, 2010
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Truly random [again]

“The measurement outputs contain at the 99% confidence level 42 new random bits. This is a much stronger statement than passing or not passing statistical tests, which merely indicate that no obvious non-random patterns are present.” arXiv:0911.3427 As often, I bought La Recherche in the station newsagent for the wrong reason! The cover of the

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New version of solaR (0.21)

December 9, 2010
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New version of solaR (0.21)

The version 0.21 of the solaR package is now available at CRAN. This package provides a set of calculation methods of solar radiation and performance of photovoltaic systems. The package has been uploaded to CRAN under the GPL-3 license. solaR is now able to calculate from both daily and sub-daily irradiation values. Besides, there are

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New paper: Survival analysis

December 8, 2010
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New paper: Survival analysis

Each year I try to carry out some statistical consultancy to give me experience in other areas of statistics and also to provide teaching examples. Last Christmas I was approached by a paediatric consultant from the RVI who wanted to carry out prospective survival analysis. The consultant, Bruce  Jaffray, had performed Nissen fundoplication surgery on

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Bayesian model selection

December 7, 2010
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Bayesian model selection

Last week, I received a box of books from the International Statistical Review, for reviewing them. I thus grabbed the one whose title was most appealing to me, namely Bayesian Model Selection and Statistical Modeling by Tomohiro Ando. I am indeed interested in both the nature of testing hypotheses or more accurately of assessing models,

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Le Monde puzzle [49]

December 7, 2010
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Le Monde puzzle [49]

Here is a quick-and-dirty solution to Le Monde puzzle posted a few days ago: the R code counts the number of winning tickets between 1 and N, and stops when there is a proportion of 10% of winning tickets. #winning ticket win=function(n){ #decimal digits decomposition x=rep(0,4) x=n%%10 m=(n-x)/10 x=m%%10 m=(m-x)/10 x=m%%10 m=(m-x)/10 x=m%%10 tic=0 for

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Bayesian adaptive sampling

December 5, 2010
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Bayesian adaptive sampling

In the continuation of my earlier post on computing evidence, I read a very interesting paper by Merlise Clyde, Joyee Ghosh and Michael Littman, to appear in JCGS. It is called  Bayesian adaptive sampling for variable selection and model averaging. The sound idea at the basis of the paper is that, when one is doing

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