If I google for “probability distribution” I find the following extremely bad picture: It’s bad because it conflates ideas and oversimplifies how variable probability distributions can generally be. Most distributions are not unimodal. Most dist...

For the longest time I resisted customizing R for my particular environment. My philosophy has been that each R script for each separate analysis I do should be self contained such that I can rerun the script from top to bottom on any machine and get the same results. This being said, I have now

While playing around with Bayesian methods for random effects models, it occured to me that inverse-Wishart priors can really bite you in the bum. Inverse Wishart-priors are popular priors over covariance functions. People like them priors because they are conjugate to a Gaussian likelihood, i.e, if you have data with each : so that the

When I fit models with interactions, I often want to recover not only the interaction effect but also the marginal effect (the main effect + the interaction) and of course the standard errors. There are a couple of ways to do this in R but I ended writ...

When I fit models with interactions, I often want to recover not only the interaction effect but also the marginal effect (the main effect + the interaction) and of course the standard errors. There are a couple of ways to do this in R but I ended writ...

When I fit models with interactions, I often want to recover not only the interaction effect but also the marginal effect (the main effect + the interaction) and of course the standard errors. There are a couple of ways to do this in R but I ended writ...