Hannah Frick

recipes

Pipeable steps for feature engineering and data preprocessing to prepare for modeling

tidymodels

Easily install and load the tidymodels packages

pkgdown

Generate static html documentation for an R package

ellmer

Call LLM APIs from R

tidyverse.org

Source of tidyverse.org

mcptools

Model Context Protocol For R

rstudio-conf

Materials for rstudio::conf

testthat

An R to make testing

yardstick

Tidy methods for measuring model performance

tidypredict

Run predictions inside the database

modeldb

Run models inside a database using R

TMwR

Code and content for "Tidy Modeling with R"

finetune

Additional functions for model tuning

parsnip

A tidy unified interface to models

rsample

Classes and functions to create and summarize resampling objects

infer

An R package for tidyverse-friendly statistical inference

tidyclust

A tidy unified interface to clustering models

lintr

Static Code Analysis for R

applicable

Quantify extrapolation of new samples given a training set

bonsai

parsnip wrappers for tree-based models

broom

Convert statistical analysis objects from R into tidy format

brulee

High-Level Modeling Functions with 'torch'

butcher

Reduce the size of model objects saved to disk

censored

Parsnip wrappers for survival models

corrr

Explore correlations in R

dials

Tools for creating tuning parameter values

discrim

Wrappers for discriminant analysis and naive Bayes models for use with the parsnip package

embed

Extra recipes for predictor embeddings

generics

Common generic methods

hardhat

Construct Modeling Packages

hex-stickers

RStudio hex stickers

learntidymodels

Learn tidymodels with interactive learnr primers

lifecycle

Manage the life cycle of your exported functions and arguments

multilevelmod

Parsnip wrappers for mixed-level and hierarchical models

planning

Documents to plan and discuss future development

probably

Tools for post-processing class probability estimates

remotes

Install R packages from GitHub, GitLab, Bitbucket, git, svn repositories, URLs

rstudio-conf-2022-program

rstudio::conf(2022, "program")

rules

parsnip extension for rule-based models

shinymodels

spatialsample

Create and summarize spatial resampling objects

stacks

An R package for tidy stacked ensemble modeling

style

The tidyverse style guide for R code

textrecipes

Extra recipes for Text Processing

themis

Extra recipes steps for dealing with unbalanced data

tidyposterior

Bayesian comparisons of models using resampled statistics

tune

Tools for tidy parameter tuning

usemodels

Boilerplate Code for tidymodels

workflows

Modeling Workflows

workflowsets

Create a collection of modeling workflows

workshops

Website and materials for tidymodels workshops

rainbowR 2026

The inaugural rainbowR virtual conference brought together LGBTQIA+ users of R, and their allies, to promote their work

IIIRqueR Plenaria Hannah Frick

Hannah Frick

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, Max Kuhn

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

Hannah Frick

Tidymodels: Now Also for Time-to-Event Data! - Hannah Frick

The tidymodels framework is a collection of packages for modeling and machine learning using tidyverse principles. In addition to regression and classification, it now also supports censored regression for time-to-event data. This type of data with potential censoring requires dedicated models and performance metrics from the field of survival analysis. While the censored package has made survival models available for a while, the recent addition of survival metrics to the yardstick package has enabled us to support this type of analysis across the entire framework. The same ease of use and vast functionality, from resampling and feature engineering to tuning, is now available for this additional modeling problem. Hannah Frick is a software engineer on the tidymodels team at Posit. She holds a PhD in statistics and has worked in interdisciplinary research and data science consultancy. She is a co-founder of R-Ladies Global

Hannah Frick

Latin R Charla plenaria Hannah Frick

Cognition: Programming Edition

Hannah Frick

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

Emil Hvitfeldt, Hannah Frick, Simon Couch

Hannah Frick - "Censored: A tidymodels package for survival models"

10 November 2022. "Censored: A tidymodels package for survival models" Hannah Frick, Software Engineer and Statistician, RStudio. CANSSI Ontario Statistical Software Conference. Abstract: Survival analysis is an important field in modeling, and there are many R packages available that implement various models, from "classic" parametric models to boosted trees. While they cover a great variety of model types, they also come with considerable amounts of heterogeneity in syntax. The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles. It provides a consistent interface to a variety of modeling functions, along with tools for resampling, assessing performance, and hyperparameter tuning. The censored package now extends the model coverage of tidymodels's parsnip package for survival analysis. It offers a tidymodels interface to parameteric survival models, (regularized) proportional hazards models and various tree-based models such as decision trees, boosted trees, and random forests for survival analysis. Additionally, it offers predictions of time to event, linear predictor, survival probability, or hazard in a tibble format consistent across different models

Hannah Frick

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

Hannah Frick

eRum 2018 - May 16 - Hannah Frick

Hannah Frick: Navigating the Wealth of R Packages

Hannah Frick