Max Kuhn

ggplot2

An implementation of the Grammar of Graphics in R

recipes

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

tidymodels

Easily install and load the tidymodels packages

usethis

Set up commonly used components

tidyverse

Easily install and load packages from the tidyverse

tidyverse.org

Source of tidyverse.org

webinars

Code and slides for RStudio webinars

ragnar

RAG in R

rlang

Low-level API for programming with 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

tibble

A modern re-imagining of the data frame

infer

An R package for tidyverse-friendly statistical inference

tidyclust

A tidy unified interface to clustering models

cheatsheets

Posit Cheat Sheets - Can also be found at https://posit.co/resources/cheatsheets/

aml-training

The most recent version of the Applied Machine Learning notes

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

keras3

R Interface to Keras

learntidymodels

Learn tidymodels with interactive learnr primers

model-implementation-principles

recommendations for creating R modeling packages

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

rules

parsnip extension for rule-based models

scales

Tools for ggplot2 scales

sessioninfo

Print Session Information

shinymodels

stacks

An R package for tidy stacked ensemble modeling

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

Data Science & AI Conference 2025

Presented by Lander Analytics, the Data Science & AI Conference brings together top data scientists, AI researchers, and industry leaders from the private and public sectors

R/Pharma 2025

R/Pharma is a scientifically & industry oriented online conference focused on the use of R in the development of pharmaceuticals

Max Kuhn - TabPFN: A Deep-Learning Solution for Tabular Data

TabPFN: A Deep-Learning Solution for Tabular Data (Max Kuhn) Abstract: There have been numerous proposals for deep neural networks for tabular data, such as rectangular data sets (e.g., data frames). To date, none have really worked well and take far too long to train. TabPFN is a model that emulates a Bayesian approach and trains a deep learning model on a prior of simulated tabular datasets. Version 2 was released this year and offers several significant advantages, but also has one notable disadvantage. I'll introduce this model and show an example. Presented at the 2025 R/Pharma Conference Europe/US Track. Resources mentioned in the presentation: - Presentation slides: https://topepo.github.io/2025-r-pharma/

Max Kuhn

Max Kuhn - Measuring LLM Effectiveness

For information on upcoming conferences, visit https://www.dataconf.ai. Measuring LLM Effectiveness by Max Kuhn Abstract: How can we quantify how accurately LLMs perform? In late 2024, Anthropic released a preprint of a manuscript about statistically analyzing model evaluations. The concepts are on target, but the statistical tactics have narrow applicability. A simpler statistical framework can be used to quantify LLM models that can be used in many more scenarios/experimental designs. We'll describe these methods and show an example. Bio: Max Kuhn is a software engineer at Posit PBC (nee RStudio). He is working on improving R’s modeling capabilities and maintaining about 30 packages, including caret. He was a Senior Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He has been applying models in the pharmaceutical and diagnostic industries for over 18 years. Max has a Ph.D. in Biostatistics. He and Kjell Johnson wrote the book Applied Predictive Modeling, which won the Ziegel award from the American Statistical Association, which recognizes the best book reviewed in Technometrics in 2015. He has co-written several other books: Feature Engineering and Selection, Tidy Models with R, and Applied Machine Learning for Tabular Data (in process). Presented at The New York Data Science & AI Conference Presented by Lander Analytics (August 27, 2025) Hosted by Lander Analytics (https://www.landeranalytics.com)

Max Kuhn

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

Bryan Shalloway - Understanding, Generating, and Evaluating Prediction Intervals

For many problems concerning prediction, providing intervals is more useful than just offering point estimates. This talk will provide an overview of: - How to think about uncertainty in your predictions (e.g., noise in the data vs uncertainty in estimation) - Approaches to producing prediction intervals (e.g., parametric vs conformal) - Measures and considerations when evaluating and training models for prediction intervals While I will touch on some similar topics as Max Kuhn’s posit::conf(2023) talk on conformal inference, my talk will cover different points and have a broader focus. I hope attendees gain an understanding of some of the key tools and concepts related to prediction intervals and that they leave inspired to learn more. Talk by Bryan Shalloway Slides: https://github.com/brshallo/posit-2024/blob/main/shalloway-posit-conf.pdf GitHub Repo: https://github.com/brshallo/posit-2024

Max Kuhn

Max Kuhn -SHINYLIVE IS SO EASY

SHINYLIVE IS SO EASY by Max Kuhn Visit https://rstats.ai for information on upcoming conferences. Abstract: shinylive is an extension to the Quarto open-source scientific and technical publishing system. It enables shiny applications to run locally, without a shiny server using WebAssembly. I’ll show examples and discuss the limitations of using shinylive. Bio: Max Kuhn is a software engineer at Posit PBC (nee RStudio). He is working on improving R’s modeling capabilities and maintaining about 30 packages, including caret. He was a Senior Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He has been applying models in the pharmaceutical and diagnostic industries for over 18 years. Max has a Ph.D. in Biostatistics. He and Kjell Johnson wrote the book Applied Predictive Modeling, which won the Ziegel award from the American Statistical Association, which recognizes the best book reviewed in Technometrics in 2015. He has co-written several other books: Feature Engineering and Selection, Tidy Models with R, and Applied Machine Learning for Tabular Data (in process). Twitter: https://twitter.com/topepos Presented at the 2024 New York R Conference (May 17, 2024) Hosted by Lander Analytics (https://landeranalytics.com)

Max Kuhn

Conformal Inference with Tidymodels - posit::conf(2023)

Presented by Max Kuhn Conformal inference theory enables any model to produce probabilistic predictions, such as prediction intervals. We'll demonstrate how these analytical methods can be used with tidymodels. Simulations will show that the results have good coverage (i.e., a 90% interval should include the real point 90% of the time). Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference. -------------------------- Talk Track: Tidy up your models. Session Code: TALK-1085

Max Kuhn

Max Kuhn - Serverless Quarto

Serverless Quarto - Max Kuhn Resources mentioned in the presentation: - Slides: https://topepo.github.io/2023-r-pharma - Example: https://topepo.github.io/shinylive-in-book-test Bio: Max Kuhn is a software engineer at Posit (née RStudio). He is working on improving R's modeling capabilities and maintaining about 30 packages, including caret. He was a Senior Director of Nonclinical Statistics at Pfizer and had been applying models in the pharmaceutical and diagnostic industries for over 18 years. Max has a Ph.D. in Biostatistics. He, and Kjell Johnson, wrote the book Applied Predictive Modeling, which won the Ziegel award from the American Statistical Association. Their second book, Feature Engineering and Selection, was published in 2019, and his book Tidy Models with R, was published in 2022. Presented at the 2023 R/Pharma Conference (October 25, 2023)

Max Kuhn

Charla Plenaria: Max Kuhn

Introduction to Tidymodels

Max Kuhn

Max Kuhn - The Post-Modeling Model to Fix the Model

The Post-Modeling Model to Fix the Model by Max Kuhn Visit https://rstats.ai/nyr to learn more. Abstract: It's possible to get a model that has good numerical performance but has predictions that are not really consistent with the data. Model calibration is a tool that can fix this. We'll show some examples of poor predictions and how different calibration tools can re-align them to the data. Bio: Max Kuhn is a software engineer at RStudio. He is currently working on improving R's modeling capabilities. He was a Senior Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He was applying models in the pharmaceutical and diagnostic industries for over 18 years. Max has a Ph.D. in Biostatistics. Max is the author of numerous R packages for techniques in machine learning and reproducible research. He, and Kjell Johnson, wrote the book Applied Predictive Modeling, which won the Ziegel award from the American Statistical Association, which recognizes the best book reviewed in Technometrics in 2015. Their second book, Feature Engineering and Selection, was published in 2019. Twitter: https://twitter.com/topepos Presented at the 2023 New York R Conference (July 14, 2023)

Max Kuhn

posit::conf(2023) Workshop: Advanced tidymodels

Register now: http://pos.it/conf Instructor: Max Kuhn, Software Engineer, Posit Workshop Duration: 1-Day Workshop This workshop is for you if you: • have used tidymodels packages like recipes, rsample, and parsnip • are comfortable with tidyverse syntax (e.g. piping, mutates, pivoting) • have some experience with resampling and modeling (e.g., linear regression, random forests, etc.), but we don’t expect you to be an expert in these In this workshop, you will learn more about model optimization using the tune and finetune packages, including racing and iterative methods. You’ll be able to do more sophisticated feature engineering with recipes. Time permitting, model ensembles via stacking will be introduced. This course is focused on the analysis of tabular data and does not include deep learning methods. Participants who have completed the “Introduction to tidymodels” workshop will be well-prepared for this course. Participants who are new to tidymodels will benefit from taking the Introduction to tidymodels workshop before joining this one

Max Kuhn

Julia Silge & Max Kuhn | Good Practices for Applied Machine Learning | Posit (2022)

The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles. Whether you are just starting out today or have years of experience with ML, tidymodels offers a consistent, flexible framework for your work. In this talk, learn how tidymodels has been designed to promote ergonomic, effective, and safe modeling practice. We will discuss how to think about the steps of building a model from beginning to end, how to fluently use different modeling and feature engineering approaches, how to avoid common pitfalls of modeling like overfitting and data leakage, and how to version and deploy reliable models trained in R. https://www.tidymodels.org/ Session: Keynote

Julia Silge, Max Kuhn

Max Kuhn | What's new in tidymodels? | RStudio

tidymodels is a collection of packages for modeling using a tidy interface. In the last year there have been numerous improvements and extensions. This talk gives an overview of additional tuning methods, new extension packages for models and recipes, and other features. About Max: Max Kuhn is a software engineer at RStudio. He is currently working on improving R's modeling capabilities. He was a Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He was applying models in the pharmaceutical and diagnostic industries for over 18 years. Max has a Ph.D. in Biostatistics. Max is the author of numerous R packages for techniques in machine learning and reproducible research and is an Associate Editor for the Journal of Statistical Software. He, and Kjell Johnson, wrote the book Applied Predictive Modeling, which won the Ziegel award from the American Statistical Association, which recognizes the best book reviewed in Technometrics in 2015. Their latest book, Feature Engineering and Selection, was published in 2019

Max Kuhn

Max Kuhn | Total Tidy Tuning Techniques | RStudio (2020)

Many models have structural parameters that cannot be directly estimated from the data. These tuning parameters can have a significant effect on model performance and require some mechanism for finding reasonable values. The tune and workflow packages enable tidymodels users to optimize these parameters using a variety of efficient grid search methods as well as with iterative search techniques (such as Bayesian optimization)

Max Kuhn

Max Kuhn | parsnip A tidy model interface | RStudio (2019)

parsnip is a new tidymodels package that generalizes model interfaces across packages. The idea is to have a single function interface for types of specific models (e.g. logistic regression) that lets the user choose the computational engine for training. For example, logistic regression could be fit with several R packages, Spark, Stan, and Tensorflow. parsnip also standardizes the return objects and sets up some new features for some upcoming packages. VIEW MATERIALS https://github.com/rstudio/rstudio-conf/tree/master/2019/Parsnip--Max_Kuhn About the Author Max Kuhn Dr. Max Kuhn is a Software Engineer at RStudio. He is the author or maintainer of several R packages for predictive modeling including caret, Cubist, C50 and others. He routinely teaches classes in predictive modeling at rstudio::conf, Predictive Analytics World, and UseR! and his publications include work on neuroscience biomarkers, drug discovery, molecular diagnostics and response surface methodology. He and Kjell Johnson wrote the award-winning book Applied Predictive Modeling in 2013

Max Kuhn