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kgoldfeld.github.io | ||
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www.rdatagen.net
Simulation can be super helpful for estimating power or sample size requirements when the study design is complex. This approach has some advantages over an analytic one (i.e.one based on a formula), particularly the flexibility it affords in setting up the specific assumptions in the planned study, such as time trends, patterns of missingness, or effects of different levels of clustering. A downside is certainly the complexity of writing the code as well as the computation time, which can be a bit painful. My goal here is to show that at least writing the code need not be overwhelming. |
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jxmo.io
A primer on variational autoencoders (VAEs) culminating in a PyTorch implementation of a VAE with discrete latents. |
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www.seascapemodels.org
[AI summary] A tutorial explaining the basics of General Linear Models using statistical concepts like linear equations, R programming simulations, and assumption checking for biological sciences. |
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indrajeetpatil.github.io
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