Software by Hannah Frick#
Events attended by Hannah Frick#
Posts and resources by Hannah Frick#
pointblank, was I expecting this? |Hannah Frick
Talk from rainbowR conference 2026: https://conference.rainbowr.org
Max Kuhn - Evaluating Time-to-Event Models is Hard
Censoring in data can frequently occur when we have a time-to-event. For example, if we order a pizza that has not yet arrived after 5 minutes, it is censored; we don’t know the final delivery time, but we know it is at least 5 minutes. Censored values can appear in clinical trials, customer churn analysis, pet adoption statistics, or anywhere a duration of time is used. I’ll describe different ways to assess models for censored data and focus on metrics requiring an evaluation time (i.e., how well does the model work at 5 minutes?). I’ll also describe how you can use tidymodel’s expanded features for these data to tell if your model fits the data well. This talk is designed to be paired with the other tidymodels talk by Hannah Frick.
Talk by Max Kuhn
Slides: https://topepo.github.io/2024-posit-conf/ GitHub Repo: https://github.com/topepo/2024-posit-conf
Hannah Frick - tidymodels for time-to-event data
Time-to-event data can show up in a broad variety of contexts: the event may be a customer churning, a machine needing repairs or replacement, a pet being adopted, or a complaint being dealt with. Survival analysis is a methodology that allows you to model both aspects, the time and the event status, at the same time. tidymodels now provides support for this kind of data across the framework.
Talk by Hannah Frick
Slides: https://hfrick.github.io/2024-posit-conf/ GitHub Repo: https://github.com/hfrick/2024-posit-conf
posit::conf(2023) Workshop: Introduction to tidymodels
Register now: http://pos.it/conf Instructors: Hannah Frick, Simon Couch, Emil Hvitfeldt Workshop Duration: 1-Day Workshop
This workshop is for you if you: • have intermediate R knowledge, experience with tidyverse packages, and either of the R pipes • can read data into R, transform and reshape data, and make a wide variety of graphs • have had some exposure to basic statistical concepts such as linear models, random forests, etc.
Intermediate or expert familiarity with modeling or machine learning is not required.
This workshop will teach you core tidymodels packages and their uses: data splitting/resampling with rsample, model fitting with parsnip, measuring model performance with yardstick, and basic pre-processing with recipes. Time permitting, you’ll be introduced to model optimization using the tune package. You’ll learn tidymodels syntax as well as the process of predictive modeling for tabular data
Hannah Frick | Censored - Survival Analysis in Tidymodels | Posit (2022)
tidymodels is extending support for survival analysis and censored is a new parsnip extension package for survival models. It offers various types of models: parametric models, semi-parametric models like the Cox model, and tree- based models like decision trees, boosted trees, and random forests. They all come with the consistent parsnip interface so that you can focus on the modelling instead of details of the syntax. Happy modelling!
Talk materials are available at https://hfrick.github.io/rstudio-conf-2022
Session: Updates from the tidymodels team
