This will be my final R Views post offering my totally idiosyncratic selection of 40 CRAN packages. It has been a good run that began in August 2016. I would like to thank all of you who followed these posts. Your readership kept me going. I hope that the “Top 40” posts were somewhat useful for helping you to keep up with the explosion of creativity in the R Community.
One hundred fifty-six new packages made it to CRAN in April. Here are my “Top 40” selections in twelve categories: Computational Methods, Data, Ecology, Economics, Genomics, Machine Learning, Mathematics, Medicine, Science, Statistics, Utilities, and Visualization.
One hundred seventy-nine new packages stuck to CRAN in March. Here are my “Top 40” picks in fifteen categories: Accounting, Computational Methods, Data, Ecology, Finance, Genomics, Machine Learning, Mathematics, Medicine, Pharma, Science, Statistics, Time Series, Utilities, and Visualization.
This post elaborates on the example given in the msm package documentation that builds a survival model for a disease where patients progress through multiple disease states that may end in death. The model is based on the theory of continuous-time Markov chains and allows for the realistic assumption that although the exact time of death may be known, patients are observed at irregular intervals to be in various intermediate states for which the exact times of transitions between states are unknown. Some care is taken to explain the relevant details of the functions used to build a model.
As a data professional, I have enjoyed learning and using multiple tools for my workflows. For me, everything used to begin and end with R. Today, SQL is a must-know. Not being able to pull your own custom tables from a warehouse can make things tricky. Then there is tidyverse, the master collection of packages for data science & analytics. As an OG R user, I cannot envision data work without tidyverse.
In this first part of a 2-part article, I want to demonstrate how a data analyst can use one OR the other for the initial stages of data exploration, and then double down on tidyverse, leveraging ggplot2 for a deeper exploration.
One hundred seventy-three new packages made it to CRAN in February. Here are my “Top 40” selections in thirteen categories: Computational Methods, Data, Ecology, Economics, Machine Learning, Mathematics, Medicine, Pharma, Science, Statistics, Time Series, Utilities, and Visualization.
One hundred sixty-five new packages made it to CRAN in January. Here are my “Top 40” selections in thirteen categories: Actuarial Statistics, Archaeology, Computational Methods, Ecology, Genomics, Mathematics, Medicine, Machine Learning, Science, Statistics, Time Series, Utilities, Visualization.
One hundred sixteen new packages stuck to CRAN in December 2022. Here are my “Top 40” selections in thirteen categories: Computational Methods, Data, Ecology, Epidemiology, Genomics, Machine Learning, Mathematics, Medicine, Networks, Signal Processing, Statistics, Utilities, and Visualization.
In this post, we share a list of upcoming conferences that either focus on the R programming language or showcase its use in the field.
One hundred sixty-seven new packages made it to CRAN in November: Here are my “Top 40” selections in fourteen categories: Climate Modeling: Computational Methods, Data, Ecology, Epidemiology, Genomics, Machine Learning, Mathematics, Networks, Pharma, Statistics, Time Series, Utilities, and Visualization. Happy New Year everyone! I hope 2023 is good to you all.