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.
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.
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.
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.
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.
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.
The third annual R / Medicine Conference was held online this year from August 27th to August 29th and was an unqualified success. The last minute pivot from small, in-person conference, which was to be held onsite at the Children’s Hospital of Philadelphia, CHOP, to a virtual event turned out to be a catalyst for positive change. Under the imaginative, and tireless leadership of conference chair Dr.
In this post, I provide some reasons for why a statistician or data scientist might want to simulate synthetic or ‘fake’ data, and briefly examine several R packages that make this task a little easier.
This post spotlights one of the most comprehensive open-source event data sets on protests, demonstrations, and other political gatherings in the contemporary U.S., along with a new GitHub repository and Shiny dashboard that make the data much easier to use and explore.
Here are my picks for the ‘Top 40’ new CRAN packages for July 2020 in seven categories: Computational Methods, Data, Genomics, Machine Learning, Science, Statistics, and Utilities.