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Programming Leftovers
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Rlang ☛ March 2025 Top 40 New CRAN Packages
In March, one hundred eighty-two new packages made it to 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.
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Underjord ☛ Underjord | Elixir is not owned by Big Tech
Originally Elixir was developed at a company, Plataformatec. Hardly a massive corporation and it was acqui-hired by Nubank in a deal which left José Valim with the rights to Elixir. So we essentially have a BDFL model (benevolent dictator for life) which seems to serve the language well.
There is a wide variety of companies using Elixir but none of them have massive input or hold particularly much sway over the development of the language. Most of the development of the ecosystem happen thanks to the efforts of lots of volunteers. Some are supported, funded or helped by the companies they work at. Some are not. Some run their own business and make time for community work (hello!).
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Rlang ☛ Model Diagnostics: Statistics vs Machine Learning
In this post, we show how different use cases require different model diagnostics. In short, we compare (statistical) inference and prediction.
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Perl / Raku
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Video ☛ Dave Cross: Still Munging Data with Perl
Join Dave Cross, author of Data Munging With Perl, for an insightful talk marking the release of the second edition, nearly 25 years after the original publication. In this session, Dave will explore the evolution of data munging practices over the last quarter-century, showcasing how Perl remains a powerful tool for tackling modern data challenges. Whether you're a seasoned Perl programmer or new to the language, this talk offers practical insights and a chance to celebrate Perl’s enduring relevance.
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Perl Hacks ☛ Data Munging with Perl (1st edition) [PDF]
Perl is so good for the extreme stuff, that we sometimes forget how powerful it is for mundane data manipulation as well. As this book so ably demonstrates, in addition to the hundreds of esoteric tools it offers, our favourite Swiss Army Chainsaw also sports a set of simple blades that are ideal for slicing and dicing ordinary data.
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Python
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Trail of Bits ☛ Making PyPI's test suite 81% faster
A robust testing suite is essential to the security and reliability of a complex codebase. However, as test coverage grows, so does execution time, creating friction in the development process and disincentivizing frequent and meaningful (i.e., deep) testing. In this post, we’ll detail how we methodically optimized the test suite for Warehouse (the back end that powers PyPI), reducing execution time from 163 seconds to 30 seconds while the test count grew from 3,900 to over 4,700.
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