VS Code is Microsoft’s open-source software development environment that can be customized for any language. It is arguably the general purpose GUI programming tool of choice when working across multiple computer languages. As a data scientist, its R - Python integration deserves careful consideration.
An attempt to capture the depth and breadth of what’s new on CRAN: here are my Top 40 picks in sixteen categories: Agriculture, Archaeology, Biology, Climate Modeling, Computational Methods, Data, Ecology, Epidemiology, Genomics, Machine Learning, Medicine, Risk Forecasting, Statistics, Time Series, Utilities, and Visualization.
This post describes a chance encounter with a time series data set for which the forecast and fable packages found different ARIMA models that don’t look much alike, but produce surprisingly close forecasts. It is a reminder of the inherent identifiability problem of ARIMA models and a record of a couple of afternoons spent down this rabbit hole.
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An attempt to capture the depth and breadth of what’s new on CRAN: Here are my Top 40 picks in fifteen categories: Artificial Intelligence, Computational Methods, Ecology, Genomics, Health Sciences, Mathematics, Machine Learning, Medicine, Music, Pharma, Statistics, Time Series, Utilities, Visualization, and Weather.
A simplified version of the four color theorem: suppose you have a 2x2 grid of squares, and you need to paint each square one of four colors: red, blue, green, or yellow. The restriction is that no two adjacent squares (sharing a side) can have the same color. How many valid ways can you color the grid?
This post presents a JAGS version of a WinBUGS model presented in the classic textbook Evidence Synthesis for Decision Making in Healthcare by Nicky J. Welton, Alexander J. Sutton, Nicola J. Cooper, Keith R. Abrams, and A.E. Ades.
An attempt to capture the depth and breadth of what’s new on CRAN.
TimeGPT is a pre-trained, multi-layer, encoder/decoder transformer model with self-attention mechanisms designed specifically for time series forecasting. This post, a revision of the of the post first published on 2025-02-12, corrects an error that deleteriously affected the ARIMA and exponential smoothing forecasts which are contrasted with the TimeGPT forecast.
In this post, I show an example of Simpson’s paradox in a logistic regression model of synthetic clinical trial data.
In a previous post, I described The Twelve Coins Problem, a notoriously hard problem that comes in many flavors and was popular on both sides of the Atlantic during World War II. In this post, I show how to build on Freeman Dyson’s solution to solve a generalization of the problem.
We share a list of upcoming conferences that either focus on the R programming language or showcase its use in the field.
In our previous post, Examining Meta Analysis, we contrasted a frequentist version of a meta analysis conducted with R’s meta package with a Bayesian meta analysis done mostly in stan using therstan package as a front end. In this post, we repeat the analysis using the brms package, which also depends on stan but allows the user to formulate complex Bayesian models without writing any stan code.
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The Twelve Coins Problem, a notoriously hard problem that comes in many flavors, was popular on both sides of the Atlantic during World War II; it was even suggested that it should be dropped over Germany in an attempt to sabotage their war effort.
A man went into a bank with 1,000 silver dollars and 10 bags. He said, ‘Place this money, please, in the bags in such a way that if I call and ask for a certain number of dollars you can hand me over one or more bags, giving me the exact amount called for without opening any of the bags.’
Let’s run some modular arithmetic using R.
Learn how to solve Bachet’s Four Weights Problem using R, with code and explanations to measure weights from 1 to 40 efficiently.
In this post we would like to review the idea of meta-analysis and compare a traditional, frequentist style, random effects meta-analysis to Bayesian methods.
One hundred eighty-one new packages made CRAN’s final cut in October.
We find more solutions to the 100 Bushels of Corn puzzle using the numbers R package.
100 bushes of corn are distributed to 100 people such that every man receives 3 bushels, every woman 2 bushels, and every child 1/2 a bushel. How many men, women, and children are there? (Solved with R).
Manifold Learning reduces data dimensions to discover patterns for analysis and visualization. This post provides an overview of Manifold Learning and its algorithms, the tsne package, and other R tools and resources.
Explore new job opportunities that highlight R skills.
Two hundred thirty new packages made it to CRAN in September. Here are my “Top 40” selections in 17 categories.
“Top 40” is back, broadcasting on the new R Works blog.
We hope that the R Works blog informs and inspires R users everywhere.
