If you haven’t explored the RStudio website in a while, your next visit may include a pleasant surprise. I recently went to the Tidymodels page, just to see what was new and was immediately drawn into the new landscape imagined by the RStudio education team. Clicking on Get Started I came to a fork and a choice of going farther with Tidymodels or backing up a bit. I went down the beginners path Finding Your Way to R and found myself in a well-lit wood, and I was not lost.

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We are in the middle of a mind-boggling natural experiment here in the United States. In spite of the advice from the CDC and dire warnings from our nation’s health care experts, millions of Americans will travel over the long holiday weekend. Although the number of people flying is significantly down from last year, there are still large numbers of Americans on the move. The TSA reported that more than two million people went through airport checkpoints last weekend, and the AAA is forecasting as many as fifty-million people may travel.

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One hundred fifty-six new packages made it to CRAN in October. Here are my “Top 40” selections in eight categories: Computational Methods, Data, Epidemiology, Mathematics, Machine Learning, Statistics, Utilities, and Visualization. Featured packages include tdaunif for sampling from manifolds, SoundShape for exploring the shape of sound, and anscombiser that hints at the potential fragility of machine learning models.

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This week, the Bay Area useR Group (BARUG) held a mini-conference focused on ROC Curves. Talks discussed the history of the ROC, extending ROC analysis to multiclass problems, various ways to think about and interpret ROC curves, and how to translate concrete business goals into the ROC framework, and pick the optimal threshold for a given problem.

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R is rich in resources for modeling and simulating survival data. In this post, I’ll focus on a small cluster of R packages (survsim, simsurv, flexsurv and survminer) that support generating biologically plausible survival data.

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Two hundred thirty-six new packages made it to CRAN in September. Here are my “Top 40” picks in eleven categories: Computational Methods, Data, Finance, Genomics, Machine Learning, Mathematics, Medicine, Statistics, Time Series, Utilities and Visualization. The large number of packages and, in my opinion, the high percentage of high quality work made choosing only forty more difficult than for most months.

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The Delphi epidemiological forecasting group at Carnegie Mellon University is undertaking a massive effort to develop leading indicators for COVID-19 outbreaks, and if you are an R or Python developer you can help. Delphi is working with both Facebook and Google to analyze the data from daily surveys that ask respondents if they (or people they know) are experiencing COVID like symptoms. The responses permit Delphi to construct a % CLI in-community signal at the county level across the United States that is being used to improve forecasts and inform public health officials.

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In a previous post, I showed some examples of simulating fake data from a few packages that are useful for common simulation tasks and indicated that I would be following up with a look at simulating survival data. A tremendous amount of work in survival analysis has been done in R1 and it will take some time to explore what’s out there. In this first post, I am just going to jump into the ocean of ideas and see if I can fish out and interesting example.

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In this final post of our six-part series, we will look at a quick and relatively easy way to provide documentation for each of the C++ functions that is exported as an R function in an Rcpp-based package.

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One hundred forty-six new packages stuck to CRAN in August. Below, are my ‘Top 40’ picks in eleven categories: Computational Methods, Data, Genomics, Insurance, Machine Learning, Mathematics, Medicine, Statistics, Time Series, Utilities and Visualization.

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