by Joseph Rickert useR! 2014 is just about two weeks away, and I am very much looking forward to meeting R users from around the world. This is just a great time to catch up with old friends, hopefully make some new friends, and talk about R and R user groups. ... [Read more...]

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by Joseph Rickert Last week,I had the opportunity to participate in the Second Academy of Science and Engineering (ASE) Conference on Big Data Science and Computing at Stanford University. Since the conference was held simultaneously with the two other conferences, one on Social Computing and the other on Cyber ... [Read more...]

by Joseph Rickert I was very happy to have been able to attend R / Finance 2014 which wrapped up a couple of weeks ago. In general, the talks were at a very high level of play, some dealing with brand new ideas and many presented at a significant level of technical ... [Read more...]

by Joseph Rickert In last week’s post, I sketched out the history of Generalized Linear Models and their implementations. In this post I’ll attempt to outline how GLM functions evolved in R to handle large data sets. The first function to make it possible to build GLM models ... [Read more...]

by Mike Bowles In two previous posts, A Thumbnail History of Ensemble Methods and Ensemble Packages in R, Mike Bowles — a machine learning expert and serial entrepreneur — laid out a brief history of ensemble methods and described a few of the many implementations in R. In this post Mike takes ... [Read more...]

by Joseph Rickert I recently wrote about some R resources that are available for generalized linear models (GLMs). Looking over the material, I was amazed by the amount of effort that is continuing to go into GLMs, both with with respect to new theoretical developments and also in response to ... [Read more...]

By Matt Sundquist Plotly's Co-Founder Here at Plotly, we are on a mission to build a platform where data scientists can analyze data, create beautiful graphs and collaborate: like a GitHub for data, where you can share and find plots, data, and code. The benefits are: Plots (including ggplot2 plots) ... [Read more...]

by Joseph Rickert R/Finance 2014 is just about a week away. Over the past four or five years this has become my favorite conference. It is small (300 people this year), exceptionally well-run, and always offers an eclectic mix of theoretical mathematics, efficient, practical computing, industry best practices and trading “street ... [Read more...]

by Seth Mottaghinejad, Analytic Consultant for Revolution Analytics In the last article, we showed two separate R implementations of the Collatz conjecture: 'nonvec_collatz' and 'vec_collatz', with the latter being more efficient than the former because of the way it takes advantage of vectorization in R. Let's once again ... [Read more...]

by Joseph Rickert Tuesday's post on a new Kaggle contest mentioned that Revolution Analytics offers a free trial for using Revolution R Enterprise in the Amazon cloud. One reason this might be of interest to contestants is the rxImport() function which reads delimited text data, fixed format text data, and ... [Read more...]

by James P. Peruvankal Kaggle just announced a competition to predict which shoppers will become repeat buyers. To aid with algorithmic development, they have provided complete, basket-level, pre-offer shopping history for a large set of shoppers who were targeted for an acquisition campaign. Files containing the incentives offered to each ... [Read more...]

by Joseph Rickert Pasha Roberts, Chief Scientist at Talent Analytics, is writing a series of articles on Employee Churn for the Predictive Analytics Times that comprise a really instructive and valuable example of using R to do some basic predictive modeling. So far, Pasha has published Employee Churn 201 in which ... [Read more...]

by Joseph Rickert One of the remarkable features of the R language is its adaptability. Motivated by R’s popularity and helped by R’s expressive power and transparency developers working on other platforms display what looks like inexhaustible creativity in providing seamless interfaces to software that complements R’s ... [Read more...]

by Daniel Hanson Last time, we looked at the four-parameter Generalized Lambda Distribution, as a method of incorporating skew and kurtosis into an estimated distribution of market returns, and capturing the typical fat tails that the normal distribution cannot. Having said that, however, the Normal distribution can be useful in ... [Read more...]

by Joseph Rickert The seven lightning talks presented to the Bay Area useR Group on Tuesday night were not only really interesting (in some cases downright entertaining) in their own right, but they also illustrated the diversity of R applications, and the extent to which R has become embedded in ... [Read more...]

by Joseph Rickert Norman Matloff professor of computer science at UC Davis, and founding member of the UCD Dept. of Statistics has begun posting as Mad(Data)Scientist. (You may know Norm from his book, The Art of R Programming: NSP, 2011.) In his second post (out today) on the new ... [Read more...]

by Mike Bowles Mike Bowles is a machine learning expert and serial entrepreneur. This is the second post in what is envisioned as a four part series that began with Mike's Thumbnail History of Ensemble Models. One of the main reasons for using R is the vast array of high-quality ... [Read more...]

by Joseph Rickert Generalized Linear Models have become part of the fabric of modern statistics, and logistic regression, at least, is a “go to” tool for data scientists building classification applications. The ready availability of good GLM software and the interpretability of the results logistic regression makes it a good ... [Read more...]

by Seth Mottaghinejad, Analytic Consultant for Revolution Analytics You may have heard before that R is a vectorized language, but what do we mean by that? One way to read that is to say that many functions in R can operate efficiently on vectors (in addition to singletons). Here are ... [Read more...]