/explore
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aosmith.rbind.io | ||
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juliasilge.com
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| | | | | A data science blog | |
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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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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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mickeystuewe.com
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| | | I'm very excited to share that my abstract was one of the 144 abstracts selected for PASS Summit 2014. This will be my first time speaking at PASS Summit and I just can't take the grin off my face.... | ||
