793 search results for "parallel"

Yet Another Forecast Dashboard

July 30, 2012
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Yet Another Forecast Dashboard

Recently, I came across quite a few examples of time series forecasting using R. Here are some examples: Time series cross-validation 4: forecasting the S&P 500 Holt-Winters forecast using ggplot2 Autoplot: Graphical Methods with ggplot2 Large-Scale Parallel Statistical Forecasting Computations in R (2011) by M. Stokely, F. Rohani, E. Tassone Forecasting time series data ARIMA

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Community Detection in Networks with R

Community Detection in Networks with R

I mainly post this visualization because I think it’s pretty. It reminds a little of the work by the famous Dutch painter Mondrian. The complete matrix can be found here. The plot is a heatmap of an adjacency matrix generated by a weighted dir...

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Revolution Analytics at JSM 2012

July 27, 2012
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Revolution Analytics is proud to once again be a gold sponsor and Wi-Fi sponsor of the JSM 2012 conference in San Diego, the largest gathering of statisticians, biostatisticians, analysts, data miners and data scientists in the world. The conference begins on Sunday, and you'll find the Revolution Analytics team in the exhibit hall. Drop by to take a look...

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R Journal, June 2012

July 20, 2012
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The June 2012 issue of the R Journal, the peer-reviewed open-journal about R packages and applications of R, is now available. This issue includes articles about: Efficiently calling C functions from R without the need for wrapper code Using clusters of Macs running Apple Xgrid for parallel distributed processing with R Semi-automated text classification with the 'maxent' package Two...

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Community Detection in Networks with R

Community Detection in Networks with R

I mainly post this visualization because I think it’s pretty. It reminds a little of the work by the famous Dutch painter Mondrian. The complete matrix can be found here. The plot is a heatmap of an adjacency matrix generated by a weighted dir...

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Data mining for network security and intrusion detection

July 16, 2012
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Data mining for network security and intrusion detection

In preparation for “Haxogreen” hackers summer camp which takes place in Luxembourg, I was exploring network security world. My motivation was to find out how data mining is applicable to network security and intrusion detection. Flame virus, Stuxnet, Duqu proved that static, signature based security systems are not able to detect very advanced, government sponsored

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Applications of R at Google

July 13, 2012
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At a talk I saw at the useR!2012 conference last month, Googler Karl Millar estimated that there are at least 200 active R users at Google, plus another 300+ occasional users participating in Google's internal R support list. But what are all these Google employees doing with R? A post from the Google Research team published on Google+ yesterday...

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Dynamical systems: Mapping chaos with R

July 13, 2012
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Dynamical systems: Mapping chaos with R

Chaos. Hectic, seemingly unpredictable, complex dynamics. In a word: fun. I usually stick to the warm and fuzzy world of stochasticity and probability distributions, but this post will be (almost) entirely devoid of randomness. While chaotic dynamics are entirely deterministic, their sensitivity to initial conditions can trick the observer into seeing iid. In ecology, chaotic

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The R Journal Volume 4/1, June 2012

July 6, 2012
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As first reported by Paolo, the new R journal is out! You can Download the complete issue from here.  Refereed articles may be downloaded individually using the links below. Table of Contents Editorial 3   Contributed Research Articles   Analysing Seasonal Data  Adrian G Barnett, Peter Baker and Annette J Dobson 5 MARSS: Multivariate Autoregressive State-space...

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Glmnet_1.8 uploaded to CRAN

July 4, 2012
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(by Trevor Hastie) Glmnet_1.8 uploaded to CRAN – This is a major revision, with two additional models included. 1) Multiresponse regression – family=”mgaussian” Here we have a matrix of M responses, and we fit a series of linear models in parallel. We use a group-lasso penalty on the set of M coefficients for each variable. This means they are...

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