Log transforms, geometric means and estimating population totals

A model that is 'improved' (in terms of making standard assumptions more plausible) by using a logarithm transform of the response will not necessarily be improved for estimating population totals.

Code for America’s Private Actions Don’t Align with Our Public Values

Recent bargaining sessions have shown that—despite public assertions to the contrary—Code for America management is not interested in bargaining with our union in good faith. Code for America Workers United was founded with one clear goal in mind: to better live out our external values internally. With our clients and partners, we listen first, act with intention, and include those who have been excluded. It’s the animating mantra of our work, threaded through our design choices, engineering projects, and communications work.

webR 0.1.0 has been released

webR 0.1.0 has been released! Using the magic of WebAssembly, webR allows you to run R code directly within a web browser.

R for Data Science, 2nd edition

The second edition of R for Data Science is out, and it's a major reworking of the first edition.

Josiah Parry - R is still fast: a salty reaction to a salty blog post

Causal foundations of bias, disparity and fairness

The study of biases, such as gender or racial biases, is an important topic in the social and behavioural sciences. However, the literature does not always clearly define the concept. Definitions of bias are often ambiguous or not provided at all. To study biases in a precise manner, it is important to have a well-defined concept of bias. We propose to define bias as a direct causal effect that is unjustified. We propose to define the closely related concept of disparity as a direct or indirect causal effect that includes a bias. Our proposed definitions can be used to study biases and disparities in a more rigorous and systematic way. We compare our definitions of bias and disparity with various criteria of fairness introduced in the artificial intelligence literature. In addition, we discuss how our definitions relate to discrimination. We illustrate our definitions of bias and disparity in two case studies, focusing on gender bias in science and racial bias in police shootings. Our proposed definitions aim to contribute to a better appreciation of the causal intricacies of studies of biases and disparities. We hope that this will also promote an improved understanding of the policy implications of such studies.

Seminar registration

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Discovering and learning everything there is to know about R packages using r-universe

The goal of r-universe is to provide a central place for browsing through the R ecosystem to discover what is out there, get a sense of the purpose and quality of individual packages, and help you get started in seconds.