Publications by R Views
Communicating results with R Markdown
In my training as a consultant, I learned that long hours of analysis were typically followed by equally long hours of preparing for presentations. I had to turn my complex analyses into recommendations, and my success as a consultant depended on my ability to influence decision makers. I used a variety of tools to convey my insights, but over ti...
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In-database xgboost predictions with R
Moving predictive machine learning algorithms into large-scale production environments can present many challenges. For example, problems arise when attempting to calculate prediction probabilities (“scores”) for many thousands of subjects using many thousands of features located on remote databases. xgboost (docs), a popular algorithm for c...
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A Mathematician’s Perspective on Topological Data Analysis and R
A few years ago, when I first became aware of Topological Data Analysis (TDA), I was really excited by the possibility that the elegant theorems of Algebraic Topology could provide some new insights into the practical problems of data analysis. But time has passed, and the sober assessment of Larry Wasserman seems to describe where things stand. ...
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Many Factor Models
Today, we will return to the Fama French (FF) model of asset returns and use it as a proxy for fitting and evaluating multiple linear models. In a previous post, we reviewed how to run the FF three-factor model on the returns of a portfolio. That is, we ran one model on one set of returns. Today, we will run multiple models on multiple streams of...
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Slack and Plumber, Part Two
This is the final entry in a three-part series about the plumber package. The first post introduces plumber as an R package for building REST API endpoints in R. The second post builds a working example of a plumber API that powers a Slack slash command. In this final entry, we will secure the API created in the previous post so that it only resp...
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October 2018: “Top 40” New Packages
One hundred eighty-five new packages made it to CRAN in October. Here are my picks for the “Top 40” in eight categories: Computational Methods, Data, Machine Learning, Medicine, Science, Statistics, Utilities, and Visualization. Computational Methods compboost v0.1.0: Provides a C++ implementation of component-wise boosting written to obtain ...
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Statistics in Glaucoma: Part I
Samuel Berchuck is a Postdoctoral Associate in Duke University’s Department of Statistical Science and Forge-Duke’s Center for Actionable Health Data Science. Joshua L. Warren is an Assistant Professor of Biostatistics at Yale University. Introduction Glaucoma is a leading cause of blindness worldwide, with a prevalence of 4% in the populati...
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Statistics in Glaucoma: Part II
Samuel Berchuck is a Postdoctoral Associate in Duke University’s Department of Statistical Science and Forge-Duke’s Center for Actionable Health Data Science. Joshua L. Warren is an Assistant Professor of Biostatistics at Yale University. Analyzing Visual Field Data In Part I of this series on statistic in glaucoma, we detailed the use of vi...
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Rsampling Fama French
Today we will continue our work on Fama French factor models, but more as a vehicle to explore some of the awesome stuff happening in the world of tidy models. For new readers who want get familiar with Fama French before diving into this post, see here where we covered importing and wrangling the data, here where we covered rolling models and vi...
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Statistics in Glaucoma: Part III
Samuel Berchuck is a Postdoctoral Associate in Duke University’s Department of Statistical Science and Forge-Duke’s Center for Actionable Health Data Science. Joshua L. Warren is an Assistant Professor of Biostatistics at Yale University. Looking Forward in Glaucoma Progression Research The contribution of the womblR package and correspondin...
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