Summarizes a Shiny app for next-word prediction using an n-gram backoff model. It describes the algorithm, explains how the app works, gives user instructions, evaluates the user experience, and highlights the project’s value as a simple but functional NLP data product.

This presentation introduces a Shiny-based PK–PD simulator that models how a drug behaves in the body over time. It explains the mathematical foundations behind the app, including one- and two-compartment pharmacokinetic models and the Emax pharmacodynamic response model. Through embedded R code and visualizations, the slides demonstrate how user-defined parameters affect drug concentration, biological response, half-life, and clearance. The goal is to provide an intuitive, interactive way to connect mathematical equations with real-world pharmacological behavior.

Interactive Ploty map with Madrid and Barcelona pointed

The document contains a map created with Leaflet.