![](https://github.com/tidymodels/recipes/raw/HEAD/man/figures/logo.png)
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.
Last updated 21 hours ago
549 stars 10.54 score 53 dependencies 335 dependentsCubist - Rule- And Instance-Based Regression Modeling
Regression modeling using rules with added instance-based corrections.
Last updated 4 days ago
38 stars 5.99 score 12 dependencies 16 dependentsmodeldata - Data Sets Useful for Modeling Examples
Data sets used for demonstrating or testing model-related packages are contained in this package.
Last updated 17 days ago
22 stars 4.12 score 18 dependencies 13 dependents![](https://github.com/tidymodels/probably/raw/HEAD/man/figures/logo.png)
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.
Last updated 1 months ago
111 stars 4.45 score 87 dependencies![](https://github.com/tidymodels/tune/raw/HEAD/man/figures/logo.png)
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, pre-processing methods, and post-processing steps.
Last updated 1 months ago
264 stars 7.15 score 83 dependencies 33 dependents![](https://github.com/tidymodels/parsnip/raw/HEAD/man/figures/logo.png)
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).
Last updated 1 months ago
564 stars 8.55 score 40 dependencies 58 dependentsplsmod - 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>).
Last updated 3 months ago
14 stars 1.97 score 61 dependencies![](https://github.com/tidymodels/baguette/raw/HEAD/man/figures/logo.png)
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.
Last updated 3 months ago
24 stars 2.53 score 66 dependencies![](https://github.com/tidymodels/tidymodels/raw/HEAD/man/figures/logo.png)
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.
Last updated 3 months ago
741 stars 8.52 score 95 dependencies 11 dependents![](https://github.com/tidymodels/finetune/raw/HEAD/man/figures/logo.png)
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) <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.
Last updated 4 months ago
61 stars 3.67 score 84 dependencies![](https://github.com/tidymodels/brulee/raw/HEAD/man/figures/logo.png)
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 as well as multilayer perceptrons.
Last updated 4 months ago
62 stars 3.57 score 44 dependenciessfd - 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.
Last updated 6 months ago
2.54 score 11 dependencies 10 dependents![](https://github.com/tidymodels/modeldb/raw/HEAD/man/figures/logo.png)
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.
Last updated 6 months ago
databasedbplyrdplyrggplot2modelingrlangsqltidyevalvisualization
80 stars 3.93 score 46 dependenciesdesirability2 - 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.
Last updated 8 months ago
7 stars 1.46 score 12 dependenciesAppliedPredictiveModeling - Functions and Data Sets for 'Applied Predictive Modeling'
A few functions and several data set for the Springer book 'Applied Predictive Modeling'.
Last updated 11 months ago
34 stars 2.71 score 20 dependenciescaret - Classification and Regression Training
Misc functions for training and plotting classification and regression models.
Last updated 1 years ago
1.6k stars 11.77 score 75 dependencies 272 dependentsusemodels - Boilerplate Code for 'Tidymodels' Analyses
Code snippets to fit models using the tidymodels framework can be easily created for a given data set.
Last updated 1 years ago
85 stars 3.95 score 85 dependenciesC50 - 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).
Last updated 1 years ago
50 stars 4.18 score 21 dependencies 12 dependentsbeans - Data on Dried Beans
These data contain morphological image measurements for dried beans from Koklu and Ozkan (2020) <doi:10.1016/j.compag.2020.105507>.
Last updated 3 years ago
1 stars 0.73 score 0 dependenciessparsediscrim - 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>.
Last updated 3 years ago
3 stars 0.99 score 34 dependenciesAmesHousing - 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.
Last updated 4 years ago
13 stars 2.20 score 16 dependencies 2 dependentssparseLDA - Sparse Discriminant Analysis
Performs sparse linear discriminant analysis for Gaussians and mixture of Gaussian models.
Last updated 8 years ago
7 stars 2.13 score 5 dependencies 3 dependentsdesirability - Function Optimization and Ranking via Desirability Functions
S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980).
Last updated 8 years ago
1 stars 1.07 score 0 dependencies 1 dependentsQSARdata - Quantitative Structure Activity Relationship (QSAR) Data Sets
Molecular descriptors and outcomes for several public domain data sets
Last updated 13 years ago
1.05 score 0 dependencies