Today in my stats class, my students saw me realize, in real-time, that you can include random intercepts in poisson models that you couldn’t in ordinary gaussian models, and this might be a nicer way to deal with overdispersion than moving to a negative binomial model.

For me, teaching stats this semester has turned into a journey of discovering what the distributional and ggdist packages can do for me. The way I make illustrative figures will never be the same. So I thought I’d revisit my post about hierarchical variance priors, this time implementing the figures using these two packages.

This might be a “everyone else already knew about this” thing, but I’ve finally gotten to a place of understanding about setting default colors scales for ggplot2, so I thought I’d share.

Recently, for class notes on probability/the central limit theorem, I wanted to recreate the table of 2d6 values that I made here. A really cool thing I found between that blog post and now is that gt has afmt_icon() operation that will replace text with its fontawesome icon.

I’ve been playing a lot of Baldur’s Gate 3 lately. My party has gotten to max level, and we’re running around, casting spells and taking names. One of the spells my wizard has is called Disintegrate , which gets summarized like this Disintegrate Shoot a thin green ray from your finger. If the spell reduces the target to 0 hit points, it disintegrates into a crumbly ash.

I’m teaching a class on quantitative methods in linguistics, and after getting tired, over the years, of debugging everyone’s computer before we could get to the content of learning R (much less quantitative methods), I decided to seize control. I’m running the course through GitHub Classroom which comes with an educational allowance for using GitHub codespaces.

I’ve been doing a lot of python development recently, and really leaning into object oriented programming for my projects. For example, in the aligned-textgrid package, I started off by defining a class to represent intervals, which at their core have start and end times, and a label.

I’ve been working a lot with the Discrete Cosine Transform in python, specifically as it’s implemented in scipy. But, I really prefer doing my data munging and stats in R. ¹ What to do! I knew the answer rested in using the reticulate package, which lets you communicate back and forth between python and R, but I hadn’t appreciated how cool reticulate was, which is why I’m making this blog post.

Last semester, I spent time co-developing some python packages: aligned-textgrid with Christian Brickhouse fasttrackpy with Santiago Barreda So, I thought I’d share a little walkthrough of a cool way to use them. They can both be installed with pip. bash pip install fastttrackpy pip install aligned-textgrid FastTrackPy fasttrackpy (Fruehwald and Barreda 2023) is a python implementation of Santiago Barreda’s Praat plugin (Barreda 2021).

I’m currently teaching a computational linguistics course, and for just the second time, I’m using Github Classroom to manage assignments. I might make a few posts about what I’ve learned in the process, but for now, I think I’ll focus on what’s gone right, and what’s been more challenging in using Github Codespaces. Github Codespaces A Github Codespace is basically a virtual machine hosted by Github with VS Code as the default interface.

I’m still thinking about priors, distributions, and logistic regressions. The fact that a fairly broad normal distribution in logit space turns into a bimodal distribution in probability space has got me thinking about the standard deviation of random effects in logistic regression.