Blog Archives

tidyposterior’s Bayesian Approach to Model Comparison

December 15, 2019
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tidyposterior’s Bayesian Approach to Model Comparison

A task common to many machine learning workflows is to compare the performance of several models with respect to some metric such as accuracy or area under the ROC curve. Standard practice is to try out several different algorithms on a training data set and see which works better. Unfortunately, all to often, after this work has been done,...

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IPO Portfolios and a Benchmark

December 10, 2019
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IPO Portfolios and a Benchmark

In two previous posts, we explored IPOs and IPO returns by sector and year since 2004 and then examined the returns of portfolios constructed by investing in IPOs each year. In today’s post, we will add a benchmark so that we can compare our IPO portfolios to something besides themselves. Next time, we will delve into return attribution to...

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In-Database Logisitc Regression with R

December 3, 2019
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In-Database Logisitc Regression with R

Roland Stevenson is a data scientist and consultant who may be reached on Linkedin. In a previous article we illustrated how to calculate xgboost model predictions in-database. This was referenced and incorporated into tidypredict. After learning more about what the tidypredict team is up to, I discovered another tidyverse package called modeldb that fits models in-database. It currently supports linear...

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Introducing sortable to add drag-and-drop to your shiny apps

November 25, 2019
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Introducing sortable to add drag-and-drop to your shiny apps

Andrie de Vries is the author of “R for Dummies” and a Solutions Engineer at RStudio Earlier this year I was a student on the RStudio Instructor Training, taught by the inspiratational Greg Wilson. I remember several tips from this course, for example that you should always try to draw a concept map of the material you are teaching. And,...

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October 2019: “Top 40” New R Packages

November 17, 2019
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October 2019: “Top 40” New R Packages

Two Hundred twenty-three new packages made it to CRAN in October. Here are my “Top 40” picks in ten categories: Computational Methods, Data, Genomics, Machine Learning, Mathematics, Medicine, Pharmacology, Statistics, Utilities, and Visualization. Computational Methods admmDensestSubmatrix v0.1.0: Implements a method to identify the densest sub-matrix in a given or sampled binary matrix. See Bombina et al. (2019) for the technical details...

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IPO Exploration Part Two

November 13, 2019
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In a previous post, we explored IPOs and IPO returns by sector and year since 2004. Today, let’s investigate how portfolios formed with those IPOs have performed. We will need to grab the price histories of the tickers, then form portfolios, then calculate their performance, and then rank those performances in some way. Since there are several hundred IPOs for...

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A comparison of methods for predicting clothing classes using the Fashion MNIST dataset in RStudio and Python (Part 1)

November 10, 2019
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A comparison of methods for predicting clothing classes using the Fashion MNIST dataset in RStudio and Python (Part 1)

Florianne Verkroost is a PhD candidate at Nuffield College at the University of Oxford. With a passion for data science and a background in mathematics and econometrics. She applies her interdisciplinary knowledge to computationally address societal problems of inequality. In this series of blog posts, I will compare different machine and deep learning methods to predict clothing categories from images...

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A First Look at Confidence Distributions

November 4, 2019
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Using a probability distribution to characterize uncertainty is at the core of statistical inference. So, it seems natural to try to summarize the information about the parameters in statistical models with probability distributions. R. A. Fisher thought so. In fact, he expended a great deal of effort over more than thirty years, and put his professional reputation on the...

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Sept 2019: “Top 40” New R Packages

October 28, 2019
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Sept 2019: “Top 40” New R Packages

One hundred and thirteen new packages made it to CRAN in September. Here are my “Top 40” picks in eight categories: Computational Methods, Data, Economics, Machine Learning, Statistics, Time Series, Utilities, and Visualization. Computational Methods eRTG3D v0.6.2: Provides functions to create realistic random trajectories in a 3-D space between two given fixed points (conditional empirical random walks), based on empirical distribution...

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IPO Exploration

October 20, 2019
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IPO Exploration

Inspired by recent headlines like “Fear Overtakes Greed in IPO Market after WeWork Debacle” and “This Year’s IPO Class is Least Profitable since the Tech Bubble”, today we’ll explore historical IPO data, and next time we’ll look at the the performance of IPO-driven portfolios constructed during the ten-year period from 2004 to 2014. I’ll admit, I’ve often wondered how...

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