This blog post will talk about Stan and how to create Stan models in R using the rstan and rstanarm packages. Although Stan provides documentation for using its programming language and a user’s guide with examples, it can be difficult to follow for a ...

Nuances of Bootstrapping Most applied statisticians and data scientists understand that bootstrapping is a method that mimics repeated sampling by drawing some number of new samples (with replacement) from the original sample in order to perform inference. However, it can be difficult to understand output from the software that carries ...

Diagnosing and fixing errors in your code can be time-consuming and frustrating. There are two ways you can make your life easier. The first is knowing the tools at your disposal in RStudio to debug errors. RStudio provides a variety of tools to help you diagnose the problem at its ...

R users adore the ggplot2 package for all things data visualization. Its consistent syntax, useful defaults, and flexibility make it a fantastic tool for creating high-quality figures. Although ggplot2 is great, there are other dataviz tools that deserve a place in a data scientist’s toolbox. Enter plotly. plotly is ...

Causality In 20th century statistics classes, it was common to hear the statement: “You can never prove causality.” As a result, researchers published results saying “x is associated with y” as a way of circumventing the issue of causality yet implicitly suggesting that the association is causal. As an example ...

While developing your R package, you will want to make sure the code it contains is as clean as possible and that your package build and testing times are as efficient as you can make them. There are a number of tricks and tools at your disposal to accomplish these ...

This post, the second part in a series that covers R package development, will define the important concept of continuous integration (CI) and demonstrate the advantages of using CI within GitLab. The version control code repository, GitLab, offers many services to its users, including the ability to set up CI ...

The best way to share your R code with others is to create a package. Whether you want to share your functions with team members, clients, or all interested R users, bundling up your functions into a package is the way to go. Luckily, there are great tools available that ...

Random forests are typically used as “black box” models for prediction, but they can return relative importance metrics associated with each feature in the model. These can be used to help interpretability and give a sense of which features are powering the predictions. Importance metrics can also assist in feature ...

Rare events are often of interest in statistics and machine learning. Mortality caused by a prescription drug may be uncommon but of great concern to patients, providers, and manufacturers. Predictive models in finance may be focused on forecasting when equities move substantially, something quite rare relative to the more quotidian ...

Logistic regression produces result that are typically interpreted in one of two ways: Predicted probabilities Odds ratios Odds are the ratio of the probability that something happens to the probabilty it doesn’t happen. \[ \Omega(X) = \frac{p(y=1|X)}{1-p(y=1|X)} \] An odds ratio is the ratio of ...

The purpose of this blog post is to review the derivation of the logit estimator and the interpretation of model estimates. Logit models are commonly used in statistics to test hypotheses related to binary outcomes, and the logistic classifier is commonly used as a pedagogic tool in machine learning courses ...