One hundred ninety-four new package made it to CRAN in August. Here are my “Top 40” picks in thirteen categories: “Computational Methods, Data, Epidemiology, Genomics, Insurance, Machine Learning, Mathematics, Medicine, Pharmaceutical Applications, Statistics, Time Series, Utilities, and Visualization.
The rtweet package makes it relatively simple to automate Twitter posts using a scheduling platform such as RStudio Connect or GitHub Actions. In this post, I work through the details using RStudio Connect.
The Cox Proportional Hazards model has so dominated survival analysis over the past forty years that I imagine quite a few people who regularly analyze survival data might assume that the Cox model, along with the Kaplan-Meier estimator and a few standard parametric models, encompass just about everything there is to say about the subject. This post introduces the counting process theory underlying survival analysis by examining a paper in which Bendix Carstensen demonstrates the equivalence of the Cox Portional Hazards model and a Poisson model.
We continue our series of posts looking into accounting and actuarial applications by showing how Sankey diagrams can be used to visualize cash flow information and hopefully improve communication with clients and decision makers who are not financial professionals.
Ninety-four new packages stuck to CRAN in July. Here are my “Top 40” selections in thirteen categories: Climate Modeling, Computational Methods, Data, Ecology, Genomics, Machine Learning, Mathematics, Medicine, Networks, Proteomics, Statistics, Utilities, and Visualization.
One hundred eighty-nine new packages made it to CRAN in June. Here are my “Top 40” selections in eleven categories: Computational Methods, Data, Ecology, Genomics, Machine Learning, Mathematics, Medicine, Statistics, Time Series, Utilities, and Visualizations.
Beginning with the invitation-only 2016 Shiny Developer Conference, Shiny has played a prominent part in all RStudio conferences, and rstudio::conf(2022) is no exception. Two workshops and eighteen talks showcase Shiny’s multiple, ever-increasing capabilities. What started out as a way to introduce R’s interactive statistical computations to the web has grown into a production-grade tool that supports serious data science workflows and facilitates the communication of data-generated insights throughout large organizations in both industry and government.
Actuarial data science lies at the intersection of math and business studies, combining statistical knowledge and methods from insurance and finance areas. Compared to data scientists, actuaries focus more on finance and business knowledge, while still collecting and analyzing data.
The profession is in high demand, and according to the Bureau of Labor Statistics (BLS), it is expected that actuary jobs will a enjoy 24% increase from 2020-30. This is much faster than the average for all occupations.
One hundred seventy-nine new packages made it to CRAN in May. Here are my “Top 40” picks in twelve categories: Computational Methods, Data, Ecology, Epidemiology, Finance, Machine Learning, Networks, Science, Statistics, Time Series, Utilities, and Visualization.
Frank Harrell`s new eBook, R Workflow, which aims to: “to foster best practices in reproducible data documentation and manipulation, statistical analysis, graphics, and reporting” is an ambitious document that is notable on multiple levels.