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www.repidemicsconsortium.org | ||
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stat.lesslikely.com
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| | | | | Computes compatibility (confidence) distributions along with their corresponding P-values, S-values, and likelihoods. The intervals can be plotted to form the distributions themselves. Functions can be compared to one another to see how much they overlap. Results can be exported to Microsoft Word, Powerpoint, and TeX documents. The package currently supports resampling methods, computing differences, generalized linear models, mixed-effects models, survival analysis, and meta-analysis. These methods are ... | |
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f1000research.com
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| | | | | Read the original article in full on F1000Research: ExploreModelMatrix: Interactive exploration for improved understanding of design matrices and linear models in R | |
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blog.r-hub.io
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| | | | | Among DESCRIPTION usual fields is the free-text URL field where package authors can store various links: to the development website, docs, upstream tool, etc. In this post, we shall explain why storing URLs in DESCRIPTION is important, where else you should add URLs and what kind of URLs are stored in CRAN packages these days. Why put URLs in DESCRIPTION? In the following we'll assume your package has some sort of online development repository (GitHub? GitLab? R-Forge?) and a documentation website (handi... | |
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kieranhealy.org
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| | | With the 2020 U.S. Census in motion already, I've been looking at various pieces of data from the Census Bureau. I decided I wanted to draw some population pyramids for the U.S. over as long a time series as I could. What's needed for that are tables for, say, as many years as possible that show the number of males and females alive at every year of age from zero to the highest age you're willing to track. This sort of data is available on the Census website. But it tuned out to be somewhat tedious to assemble into a single usable series. (Perhaps it's available in an easy-to-digest form elsewhere, but I couldn't find it.) I initially worked with a couple of the excellent R packages that talk to the Census API (tidycensus and censusapi), hoping they'd give me what I needed. But in the end I wrangled an annual year-of-age series from 1900 to 2019 by grabbing the data from the Census and cleaning it myself. As always, 95% of data analysis is in fact data acquisition and data cleaning. | ||
