(Still on my math streak.) 1994 had an exciting moment when Fred Galvin solved the 1979 Jeff Dinitz conjecture on list-coloring Latin squares. Latin squares are a simple predecessor to puzzles such as Soduko. A Latin square is an n by n grid of the integers 0 through n-1 (called […]

Estimated reading time: 9 minutes

I’ve been tinkering a lot recently with the data_algebra, and just released version 0.7.0 to PyPi. In this note I’ll touch on what the data algebra is, what the new features are, and my plans going forward.

Estimated reading time: 10 minutes

One often hears that R can not be fast (false), or more correctly that for fast code in R you may have to consider “vectorizing.” A lot of knowledgable R users are not comfortable with the term “vectorize”, and not really familiar with the method. “Vectorize” is just a slightly […]

Estimated reading time: 7 minutes

We try to keep this blog mostly technical and business (as we assume that is what our readers are here for). However, this post is going to be an exception. I’ve just got back from photographing the Rotary Club of San Francisco‘s 2018 Holiday Party. We had a special guest […]

Estimated reading time: 1 minute

R is designed to make working with statistical models fast, succinct, and reliable. For instance building a model is a one-liner: model <- lm(Petal.Length ~ Sepal.Length, data = iris) And producing a detailed diagnostic summary of the model is also a one-liner: summary(model) # Call: # lm(formula = Petal.Length ~ […]

Estimated reading time: 1 minute

We’ve just finished off a series of articles on some recent research results applying differential privacy to improve machine learning. Some of these results are pretty technical, so we thought it was worth working through concrete examples. And some of the original results are locked behind academic journal paywalls, so […]

Estimated reading time: 1 minute