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aosmith.rbind.io | ||
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www.rdatagen.net
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| | | | | Inspired by a free online course titled Complier Average Causal Effects (CACE) Analysis and taught by Booil Jo and Elizabeth Stuart (through Johns Hopkins University), I've decided to explore the topic a little bit. My goal here isn't to explain CACE analysis in extensive detail (you should definitely go take the course for that), but to describe the problem generally and then (of course) simulate some data. A plot of the simulated data gives a sense of what we are estimating and assuming. | |
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swingleydev.com
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| | | | | [AI summary] The author uses SQL, R, and Arduino programming to analyze historical temperature and snowfall data in Fairbanks, Alaska, to investigate trends in the timing and intensity of extreme cold and snow-free periods. | |
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easystats.github.io
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| | | | | You probably already have heard of the parameters package, a light-weight package to extract, compute and explore the parameters of statistical models using R (if not, there is a related publication introducing the package's main features). In this post, we like to introduce a new feature that facilitates nicely rendered output in markdown or HTML format (including PDFs). This allows you to easily create pretty tables of model summaries, for a large variety of models. | |
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fharrell.com
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| | | Many researchers worry about violations of the proportional hazards assumption when comparing treatments in a randomized study. Besides the fact that this frequently makes them turn to a much worse approach, the harm done by violations of the proportional odds assumption usually do not prevent the proportional odds model from providing a reasonable treatment effect assessment. | ||
