Welcome to the second installment of Reproducible Finance 2019!
In the previous post, we looked back on the daily returns for several market sectors in 2018. Today, we’ll continue that theme and look at some summary statistics for 2018, and then extend out to previous years and different ways of visualizing our data.
This interview came about through researching R-based medical applications in preparation for the upcoming R/Medicine conference. When we discovered the impressive number of Shiny-based Risk Calculators developed by the Cleveland Clinic and implemented in public-facing sites, we wanted to learn more about the influence of R Language in the development of statistical science at this prominent institution. We were fortunate to have Jarrod Dalton of the Quantitative Health Sciences Department grant this interview.
By my count, 157 new packages stuck to CRAN in December. Below are my “Top 40” picks in ten categories: Computational Methods, Data, Finance, Machine Learning, Medicine, Science, Statistics, Time Series, Utilities and Visualization. This is the first time I have used the Medicine category. I am pleased that a few packages that appear to have clinical use made the cut. Also noteworthy in this month’s selection are the inclusion of four packages from the Microsoft Azure team (stuffing 41 packages into the “Top 40”), and some eclectic, but fascinating packages in the Science section.
Harrison Schramm is a Professional Statistician and Non-Resident Senior Fellow at the Center for Strategic and Budgetary Assessments.
The Shiny set of tools, and, by extension, Flexdashboard, give professional analysts tools to rapidly put interactive versions of their work in the hands of clients. Frequently, an end user will interact with data by either uploading or downloading a new set in its entirety (typically from a .csv or other similarly structured source), or do so ‘on the fly’ interactively, using tools like RHandsonTable.
I have been thinking about writing a short post on R resources for working with (ROC) curves, but first I thought it would be nice to review the basics. In contrast to the usual (usual for data scientists anyway) machine learning point of view, I’ll frame the topic closer to its historical origins as a portrait of practical decision theory.
ROC curves were invented during WWII to help radar operators decide whether the signal they were getting indicated the presence of an enemy aircraft or was just noise.
Welcome to Reproducible Finance 2019! It’s a new year, a new beginning, the Earth has completed one more trip around the sun, and that means it’s time to look back on the previous January to December cycle.
2018 was a good year for R Views. With a total of sixty-three posts for the year, we exceeded the pace of at least one post per week. But, it wasn’t quantity we were shooting for. Our main goal was, and continues to be, featuring thoughtful commentary on topics of interest to the R Community and in-depth technical elaboration of R language applications.
Before highlighting a few of my favorite posts for 2018, I would like to express my profound gratitude to our guest bloggers (R Community members who are not employed at RStudio), our regular RStudio contributors who sparkled with creativity while meeting committed deadlines, and you, our readers, who made it all worthwhile.
Today, we continue our work on sampling so that we can run models on subsets of our data and then test the accuracy of the models on data not included in those subsets. In the machine learning prediction world, these two data sets are often called training data and testing data, but we’re not going to do any machine learning prediction today. We’ll stay with our good’ol Fama French regression models for the reasons explained last time: the goal is to explore a new method of sampling our data and I prefer to do that in the context of a familiar model and data set.
Having absorbed an average of 181 new packages each month over the last 28 months, CRAN is still growing at a pretty amazing rate. The following plot shows the number of new packages since I started keeping track in August 2016.
This November, 171 new packages stuck to CRAN. Here is my selection for the “Top 40” organized into the categories: Computational Methods, Data, Finance, Machine Learning, Marketing Analytics, Science, Statistics, Utilities and Visualization.
This blog post is the third installment of a three-part series that introduces the role of statistical methods in glaucoma disease management, and the importance of software in glaucoma research. Part I provides an introduction to glaucoma and the use of visual fields for diagnosis purposes. Part II provides a case study applying a novel Bayesian method to learn about glaucoma progression and its use clinically. Finally, Part III details future directions for statistics in glaucoma, and the importance of accessible software for use in clinical practice.