Publications by Joseph Rickert
Fun with ddR: Using Distributed Data Structures in R
by Edward Ma and Vishrut Gupta (Hewlett Packard Enterprise) A few weeks ago, we revealed ddR (Distributed Data-structures in R), an exciting new project started by R-Core, Hewlett Packard Enterprise, and others that provides a fresh new set of computational primitives for distributed and parallel computing in R. The package sets the seed for what...
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Wald’s graphical sequential inspection procedure
by John Mount Ph.D.Data Scientist at Win-Vector LLC Our most recent article was a dynamic programming solution to the A/B test problem. Explicitly solving such dynamic programs is a long and tedious process, so you are well served by finding and introducing clever invariants to track (something better than just raw win-rates). This clever idea, ...
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Trade-offs to consider when reading a large dataset into R using the RevoScaleR package
by Seth Mottaghinejad, Data Scientist at Microsoft R and big data There are many R packages dedicated to letting users (or useRs if you prefer) deal with big data in R. (We will intentionally avoid using proper case for 'big data', because (1) the term has been somewhat hackneyed, and (2) for the sake of this article we can think of big data as a...
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Looking forward to 2016
by Joseph Rickert The following map of all of the R user groups listed in Microsoft's Local R User Group Directory is good way to visualize the R world as we rocket into 2016. As a member of the useR!2016 planning committee, foremost in my mind right now is that in just a few months people will be coming to Stanford from all points plotted and al...
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7th Meeting of Spanish R Users. 5-6 November 2015. Salamanca (Spain)
By Virgilio Gómez Rubio, Spanish R Users Organizing Committee As every autumn since 2009, Spanish R users gathered at their annual meeting. It is organised by Spanish R users group ‘Comunidad R-Hispano’and took place in 5-6 November in the historic city of Salamanca. The 7th Meeting of Spanish R Users attracted more than 100 R entusiasts and...
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Getting Started with Markov Chains
by Joseph Rickert There are number of R packages devoted to sophisticated applications of Markov chains. These include msm and SemiMarkov for fitting multistate models to panel data, mstate for survival analysis applications, TPmsm for estimating transition probabilities for 3-state progressive disease models, heemod for applying Markov models...
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Microsoft R Server available free to students with DreamSpark
by Joseph Rickert Over the last 6 years, thousands of students and faculty have downloaded Revolution R Enterprise (RRE) from Revolution Analytics for free, making it possible for them to do statistical modeling on large data sets with the same R language used by savvy statisticians and data scientists in business and industry. In addition to t...
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New Data Sources for R
by Joseph Rickert Over the past few months, a number of new CRAN packages have appeared that make it easier for R users to gain access to curated data. Most of these provide interfaces to a RESTful API written by the data publishers while a few just wrap the data set inside the package. Some of the new packages are only very simple, one function ...
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A gentle introduction to parallel computing in R
by John Mount Ph.D.Data Scientist at Win-Vector LLC Let's talk about the use and benefits of parallel computation in R. IBM's Blue Gene/P massively parallel supercomputer (Wikipedia). Parallel computing is a type of computation in which many calculations are carried out simultaneously.” Wikipedia quoting: Gottlieb, Allan; Almasi, George S. (1...
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Getting Started with Markov Chains: Part 2
by Joseph Rickert In a previous post, I showed some elementary properties of discrete time Markov Chains could be calculated, mostly with functions from the markovchain package. In this post, I would like to show a little bit more of the functionality available in that package by fitting a Markov Chain to some data. In this first block of code, I...
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