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caret - Classification and Regression Training

Misc functions for training and plotting classification and regression models.

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19.94 score 1.7k stars 327 dependents 89k scripts 102k downloads

recipes - Preprocessing and Feature Engineering Steps for Modeling

A recipe prepares your data for modeling. We provide an extensible framework for pipeable sequences of feature engineering steps provides preprocessing tools to be applied to data. Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting processed output can then be used as inputs for statistical or machine learning models.

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quarto

18.16 score 625 stars 425 dependents 7.7k scripts 135k downloads

tidymodels - Easily Install and Load the 'Tidymodels' Packages

The tidy modeling "verse" is a collection of packages for modeling and statistical analysis that share the underlying design philosophy, grammar, and data structures of the tidyverse.

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quarto

16.80 score 819 stars 15 dependents 81k scripts 94k downloads

parsnip - A Common API to Modeling and Analysis Functions

A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', 'H2O', etc).

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16.54 score 660 stars 85 dependents 3.7k scripts 35k downloads

tune - Tidy Tuning Tools

The ability to tune models is important. 'tune' contains functions and classes to be used in conjunction with other 'tidymodels' packages for finding reasonable values of hyper-parameters in models, preprocessing methods, and post-processing steps.

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14.62 score 344 stars 51 dependents 1.4k scripts 26k downloads

Cubist - Rule- And Instance-Based Regression Modeling

Regression modeling using rules with added instance-based corrections.

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12.82 score 45 stars 18 dependents 3.0k scripts 12k downloads

probably - Tools for Post-Processing Predicted Values

Models can be improved by post-processing class probabilities, by: recalibration, conversion to hard probabilities, assessment of equivocal zones, and other activities. 'probably' contains tools for conducting these operations as well as calibration tools and conformal inference techniques for regression models.

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12.08 score 123 stars 1 dependents 24k scripts 3.8k downloads

butcher - Model Butcher

Provides a set of S3 generics to axe components of fitted model objects and help reduce the size of model objects saved to disk.

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11.83 score 139 stars 18 dependents 196 scripts 6.4k downloads

C50 - C5.0 Decision Trees and Rule-Based Models

C5.0 decision trees and rule-based models for pattern recognition that extend the work of Quinlan (1993, ISBN:1-55860-238-0).

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11.73 score 53 stars 11 dependents 1.5k scripts 7.0k downloads

stacks - Tidy Model Stacking

Model stacking is an ensemble technique that involves training a model to combine the outputs of many diverse statistical models, and has been shown to improve predictive performance in a variety of settings. 'stacks' implements a grammar for 'tidymodels'-aligned model stacking.

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11.40 score 302 stars 2 dependents 992 scripts 2.0k downloads

modeldata - Data Sets Useful for Modeling Examples

Data sets used for demonstrating or testing model-related packages are contained in this package.

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11.13 score 25 stars 18 dependents 2.9k scripts 29k downloads

finetune - Additional Functions for Model Tuning

The ability to tune models is important. 'finetune' enhances the 'tune' package by providing more specialized methods for finding reasonable values of model tuning parameters. Two racing methods described by Kuhn (2014) <doi:10.48550/arXiv.1405.6974> are included. An iterative search method using generalized simulated annealing (Bohachevsky, Johnson and Stein, 1986) <doi:10.1080/00401706.1986.10488128> is also included.

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9.49 score 65 stars 2 dependents 1.1k scripts 2.4k downloads

tidyposterior - Bayesian Analysis to Compare Models using Resampling Statistics

Bayesian analysis used here to answer the question: "when looking at resampling results, are the differences between models 'real'?" To answer this, a model can be created were the performance statistic is the resampling statistics (e.g. accuracy or RMSE). These values are explained by the model types. In doing this, we can get parameter estimates for each model's affect on performance and make statistical (and practical) comparisons between models. The methods included here are similar to Benavoli et al (2017) <https://jmlr.org/papers/v18/16-305.html>.

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8.78 score 103 stars 303 scripts 640 downloads

tailor - Iterative Steps for Postprocessing Model Predictions

Postprocessors refine predictions outputted from machine learning models to improve predictive performance or better satisfy distributional limitations. This package introduces 'tailor' objects, which compose iterative adjustments to model predictions. A number of pre-written adjustments are provided with the package, such as calibration. See Lichtenstein, Fischhoff, and Phillips (1977) <doi:10.1007/978-94-010-1276-8_19>. Other methods and utilities to compose new adjustments are also included. Tailors are tightly integrated with the 'tidymodels' framework.

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8.61 score 18 stars 52 dependents 42 scripts 23k downloads

brulee - High-Level Modeling Functions with 'torch'

Provides high-level modeling functions to define and train models using the 'torch' R package. Models include linear, logistic, and multinomial regression, multilayer perceptrons, modern neural neural networks, and the pretrained tabular foundational models such as 'TabICL' and 'TabPFN'.

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8.05 score 75 stars 217 scripts 1.1k downloads

AmesHousing - The Ames Iowa Housing Data

Raw and processed versions of the data from De Cock (2011) <http://ww2.amstat.org/publications/jse> are included in the package.

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7.66 score 15 stars 2 dependents 690 scripts 3.7k downloads

modeldb - Fits Models Inside the Database

Uses 'dplyr' and 'tidyeval' to fit statistical models inside the database. It currently supports KMeans and linear regression models.

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databasedbplyrdplyrggplot2modelingrlangsqltidyevalvisualization

7.61 score 80 stars 64 scripts 438 downloads

baguette - Efficient Model Functions for Bagging

Tree- and rule-based models can be bagged (<doi:10.1007/BF00058655>) using this package and their predictions equations are stored in an efficient format to reduce the model objects size and speed.

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7.12 score 29 stars 1.0k scripts 798 downloads

usemodels - Boilerplate Code for 'Tidymodels' Analyses

Code snippets to fit models using the tidymodels framework can be easily created for a given data set.

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7.11 score 88 stars 185 scripts 310 downloads

AppliedPredictiveModeling - Functions and Data Sets for 'Applied Predictive Modeling'

A few functions and several data set for the Springer book 'Applied Predictive Modeling'.

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6.94 score 34 stars 1.5k scripts 3.4k downloads

sfd - Space-Filling Design Library

A collection of pre-optimized space-filling designs, for up to ten parameters, is contained here. Functions are provided to access designs described by Husslage et al (2011) <doi:10.1007/s11081-010-9129-8> and Wang and Fang (2005) <doi:10.1142/9789812701190_0040>. The design types included are Audze-Eglais, MaxiMin, and uniform.

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6.43 score 73 dependents 5 scripts 24k downloads

tabpfn - Prior-Data Fitted Network Foundational Model for Tabular Data

Provides a consistent API for classification and regression models based on the 'TabPFN' model of Hollmann et al. (2025), "Accurate predictions on small data with a tabular foundation model," Nature, 637(8045) <doi:10.1038/s41586-024-08328-6>. The calculations are served via 'Python' to train and predict the model.

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6.33 score 34 stars 26 scripts 586 downloads

desirability2 - Desirability Functions for Multiparameter Optimization

In-line functions for multivariate optimization via desirability functions (Derringer and Suich, 1980, <doi:10.1080/00224065.1980.11980968>) with easy use within 'dplyr' pipelines.

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6.18 score 15 stars 2 dependents 68 scripts 619 downloads

plsmod - Model Wrappers for Projection Methods

Bindings for additional regression models for use with the 'parsnip' package, including ordinary and spare partial least squares models for regression and classification (Rohart et al (2017) <doi:10.1371/journal.pcbi.1005752>).

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mixomics

6.01 score 14 stars 104 scripts 514 downloads

shinymodels - Interactive Assessments of Models

Launch a 'shiny' application for 'tidymodels' results. For classification or regression models, the app can be used to determine if there is lack of fit or poorly predicted points.

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shiny

5.87 score 50 stars 42 scripts 330 downloads

sparseLDA - Sparse Discriminant Analysis

Performs sparse linear discriminant analysis for Gaussians and mixture of Gaussian models.

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5.46 score 7 stars 2 dependents 37 scripts 1.9k downloads

desirability - Function Optimization and Ranking via Desirability Functions

S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980).

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5.29 score 3 stars 1 dependents 43 scripts 649 downloads

sparsediscrim - Sparse and Regularized Discriminant Analysis

A collection of sparse and regularized discriminant analysis methods intended for small-sample, high-dimensional data sets. The package features the High-Dimensional Regularized Discriminant Analysis classifier from Ramey et al. (2017) <arXiv:1602.01182>. Other classifiers include those from Dudoit et al. (2002) <doi:10.1198/016214502753479248>, Pang et al. (2009) <doi:10.1111/j.1541-0420.2009.01200.x>, and Tong et al. (2012) <doi:10.1093/bioinformatics/btr690>.

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5.00 score 3 stars 1 dependents 116 scripts 1.9k downloads

tabby - Tidy Interfaces for Tabular Deep Learning

Model definitions for fitting, tuning, and predicting with deep learning models for tabular data using the 'parsnip' and 'tidymodels' frameworks. Supported models include the TabPFN foundation model of Hollmann et al. (2025) <doi:10.1038/s41586-024-08328-6>, TabICL by Qu et al. (2025) <doi:10.48550/arXiv.2502.05564>, SAINT by Somepalli et al. (2021) <doi:10.48550/arXiv.2106.01342>, and the tabular ResNet of Gorishniy et al. (2021) <doi:10.48550/arXiv.2106.11959>, among others. Helper functions are also included to create space-filling grids for tuning parameters that are defined on a per-layer basis. These functions define the model interfaces; the computations are carried out by separate engine packages built on 'torch'.

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4.83 score 4 stars 31 scripts 100 downloads

important - Supervised Feature Selection

Interfaces for choosing important predictors in supervised regression, classification, and censored regression models. Permuted importance scores (Biecek and Burzykowski (2021) <doi:10.1201/9780429027192>) can be computed for 'tidymodels' model fits.

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4.65 score 19 stars 47 scripts 431 downloads