Large healthcare databases are increasingly used for research investigating the effects of medications. However, a key challenge is capturing hard-to-measure concepts (often relating to frailty and disease severity) that can be crucial for successful confounder adjustment. The high-dimensional propensity score has been proposed as a data-driven method to improve confounder adjustment within healthcare databases and was developed in the context of administrative claims databases. We present hdps, a suite of commands implementing this approach in Stata that assesses the prevalence of codes, generates high-dimensional propensity-score covariates, performs variable selection, and provides investigators with graphical tools for inspecting the properties of selected covariates.