R

Lasso & glinternet Every Data Scientist and her dog know linear and logistic regression. The majority will probably also know that these models have regularized versions, which increase predictive performance by reducing variance (at the cost of a small increase in bias). Choosing L1-regularization (Lasso) even gets you variable selection for free. The theory behind these models is covered expertly in The Elements of Statistical Learning (for an easier version, see An Introduction to Statistical Learning), and implemented nicely in the packages glmnet for R and scikitlearn for Python.

Models extremes of bursty time series.

The R package is now available on CRAN. It Models extremes of ‘bursty’ time series via Continuous Time Random Exceedances (CTRE). (See companion paper.)

First version of an R package which calculates probability densities of the Mittag-Leffler families of distributions.

Computes Mittag-Leffler probability densities.

Only ¹⁄₂ – ²⁄₃ of antidepressant users have depression. How do we use statistical learning to predict depression given antidepressant use?