Publications by Joseph Rickert
Importing a log file with rxImport()
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 with an appropriate ODBC driver, data st...
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R and the Collatz Conjecture: Part 2
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 take a look at 'vec_coll...
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R and Finance
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 smarts”. This...
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Plotly and rOpenSci: Make ggplots shareable and interactive.
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) are interactive and drawn with D3 (try zooming...
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Quick History: glm()
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 practical problems such as the need to deal with very ...
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Ensemble Methods Part 3: Revolution Analytics Big Data Random Forest Function
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 a detailed look at the Random Forests imple...
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Quick History 2: GLMs, R and large data sets
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 with datasets that are too big to fit into memory was the bigglm()fr...
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R / Finance 2014: Packaged Takeaways
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 or mathematical sophistication. Fortunately, most of the slides from the presentat...
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Deep Learning at Stanford
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 Security, it was definitely not an ...
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Quantitative Finance Applications in R – 6: Constructing a Term Structure of Interest Rates Using R (Part 1)
by Daniel Hanson Introduction Last time, we used the discretization of a Brownian Motion process with a Monte Carlo method to simulate the returns of a single security, with the (rather strong) assumption of a fixed drift term and fixed volatility. We will return to this topic in a future article, as it relates to basic option pricing methods, ...
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