tidyclust - A Common API to Clustering
A common interface to specifying clustering models, in the same style as 'parsnip'. Creates unified interface across different functions and computational engines.
Last updated 20 hours ago
106 stars 4.43 score 91 dependencies
bonsai - Model Wrappers for Tree-Based Models
Bindings for additional tree-based model engines for use with the 'parsnip' package. Models include gradient boosted decision trees with 'LightGBM' (Ke et al, 2017.), conditional inference trees and conditional random forests with 'partykit' (Hothorn and Zeileis, 2015. and Hothorn et al, 2006. <doi:10.1198/106186006X133933>), and accelerated oblique random forests with 'aorsf' (Jaeger et al, 2022 <doi:10.5281/zenodo.7116854>).
Last updated 21 hours ago
45 stars 3.23 score 44 dependenciesorbital - Predict with 'tidymodels' Workflows in Databases
Turn 'tidymodels' workflows into objects containing the sufficient sequential equations to perform predictions. These smaller objects allow for low dependency prediction locally or directly in databases.
Last updated 1 days ago
1 stars 1.09 score 2 dependenciesmodeldata - Data Sets Useful for Modeling Examples
Data sets used for demonstrating or testing model-related packages are contained in this package.
Last updated 14 days ago
22 stars 4.12 score 18 dependencies 13 dependents
dials - Tools for Creating Tuning Parameter Values
Many models contain tuning parameters (i.e. parameters that cannot be directly estimated from the data). These tools can be used to define objects for creating, simulating, or validating values for such parameters.
Last updated 16 days ago
111 stars 6.13 score 27 dependencies 45 dependents
agua - 'tidymodels' Integration with 'h2o'
Create and evaluate models using 'tidymodels' and 'h2o' <https://h2o.ai/>. The package enables users to specify 'h2o' as an engine for several modeling methods.
Last updated 21 days ago
21 stars 2.39 score 87 dependencies
censored - 'parsnip' Engines for Survival Models
Engines for survival models from the 'parsnip' package. These include parametric models (e.g., Jackson (2016) <doi:10.18637/jss.v070.i08>), semi-parametric (e.g., Simon et al (2011) <doi:10.18637/jss.v039.i05>), and tree-based models (e.g., Buehlmann and Hothorn (2007) <doi:10.1214/07-STS242>).
Last updated 21 days ago
parsniptidymodels
123 stars 4.73 score 69 dependencies
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.
Last updated 22 days ago
285 stars 6.21 score 88 dependencies 1 dependents
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 26 days ago
546 stars 11.61 score 53 dependencies 592 dependents
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 28 days ago
110 stars 4.49 score 86 dependencies
workflows - Modeling Workflows
Managing both a 'parsnip' model and a preprocessor, such as a model formula or recipe from 'recipes', can often be challenging. The goal of 'workflows' is to streamline this process by bundling the model alongside the preprocessor, all within the same object.
Last updated 29 days ago
198 stars 7.03 score 42 dependencies 65 dependents
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 30 days ago
264 stars 7.15 score 83 dependencies 33 dependents
rsample - General Resampling Infrastructure
Classes and functions to create and summarize different types of resampling objects (e.g. bootstrap, cross-validation).
Last updated 1 months ago
328 stars 7.86 score 30 dependencies 70 dependents
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 dependents
broom - Convert Statistical Objects into Tidy Tibbles
Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.
Last updated 2 months ago
modelingtidy-data
1.4k stars 14.47 score 22 dependencies 1432 dependentsshinymodels - 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.
Last updated 2 months ago
shiny
46 stars 3.19 score 123 dependencies
hardhat - Construct Modeling Packages
Building modeling packages is hard. A large amount of effort generally goes into providing an implementation for a new method that is efficient, fast, and correct, but often less emphasis is put on the user interface. A good interface requires specialized knowledge about S3 methods and formulas, which the average package developer might not have. The goal of 'hardhat' is to reduce the burden around building new modeling packages by providing functionality for preprocessing, predicting, and validating input.
Last updated 2 months ago
100 stars 9.98 score 11 dependencies 392 dependents
yardstick - Tidy Characterizations of Model Performance
Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).
Last updated 3 months ago
365 stars 7.79 score 17 dependencies 54 dependents
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.
Last updated 3 months ago
130 stars 5.22 score 16 dependencies 12 dependents
poissonreg - Model Wrappers for Poisson Regression
Bindings for Poisson regression models for use with the 'parsnip' package. Models include simple generalized linear models, Bayesian models, and zero-inflated Poisson models (Zeileis, Kleiber, and Jackman (2008) <doi:10.18637/jss.v027.i08>).
Last updated 3 months ago
22 stars 2.51 score 41 dependencies 1 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
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 dependenciesdiscrim - Model Wrappers for Discriminant Analysis
Bindings for additional classification models for use with the 'parsnip' package. Models include flavors of discriminant analysis, such as linear (Fisher (1936) <doi:10.1111/j.1469-1809.1936.tb02137.x>), regularized (Friedman (1989) <doi:10.1080/01621459.1989.10478752>), and flexible (Hastie, Tibshirani, and Buja (1994) <doi:10.1080/01621459.1994.10476866>), as well as naive Bayes classifiers (Hand and Yu (2007) <doi:10.1111/j.1751-5823.2001.tb00465.x>).
Last updated 3 months ago
28 stars 2.57 score 43 dependencies
rules - Model Wrappers for Rule-Based Models
Bindings for additional models for use with the 'parsnip' package. Models include prediction rule ensembles (Friedman and Popescu, 2008) <doi:10.1214/07-AOAS148>, C5.0 rules (Quinlan, 1992 ISBN: 1558602380), and Cubist (Kuhn and Johnson, 2013) <doi:10.1007/978-1-4614-6849-3>.
Last updated 3 months ago
39 stars 3.02 score 43 dependencies 1 dependents
textrecipes - Extra 'Recipes' for Text Processing
Converting text to numerical features requires specifically created procedures, which are implemented as steps according to the 'recipes' package. These steps allows for tokenization, filtering, counting (tf and tfidf) and feature hashing.
Last updated 3 months ago
157 stars 5.17 score 56 dependencies 1 dependentsmodeldatatoo - More Data Sets Useful for Modeling Examples
More data sets used for demonstrating or testing model-related packages are contained in this package. The data sets are downloaded and cached, allowing for more and bigger data sets.
Last updated 3 months ago
7 stars 1.86 score 29 dependencies
infer - Tidy Statistical Inference
The objective of this package is to perform inference using an expressive statistical grammar that coheres with the tidy design framework.
Last updated 3 months ago
707 stars 8.45 score 39 dependencies 13 dependents
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 dependentsworkflowsets - Create a Collection of 'tidymodels' Workflows
A workflow is a combination of a model and preprocessors (e.g, a formula, recipe, etc.) (Kuhn and Silge (2021) <https://www.tmwr.org/>). In order to try different combinations of these, an object can be created that contains many workflows. There are functions to create workflows en masse as well as training them and visualizing the results.
Last updated 3 months ago
88 stars 5.12 score 84 dependencies 16 dependents
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 3 months ago
61 stars 3.67 score 84 dependencies
embed - Extra Recipes for Encoding Predictors
Predictors can be converted to one or more numeric representations using a variety of methods. Effect encodings using simple generalized linear models <arXiv:1611.09477> or nonlinear models <arXiv:1604.06737> can be used. There are also functions for dimension reduction and other approaches.
Last updated 4 months ago
141 stars 4.98 score 68 dependencies
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 dependencies
themis - Extra Recipes Steps for Dealing with Unbalanced Data
A dataset with an uneven number of cases in each class is said to be unbalanced. Many models produce a subpar performance on unbalanced datasets. A dataset can be balanced by increasing the number of minority cases using SMOTE 2011 <arXiv:1106.1813>, BorderlineSMOTE 2005 <doi:10.1007/11538059_91> and ADASYN 2008 <https://ieeexplore.ieee.org/document/4633969>. Or by decreasing the number of majority cases using NearMiss 2003 <https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf> or Tomek link removal 1976 <https://ieeexplore.ieee.org/document/4309452>.
Last updated 4 months ago
140 stars 4.97 score 56 dependencies 1 dependentsmodelenv - Provide Tools to Register Models for Use in 'tidymodels'
An developer focused, low dependency package in 'tidymodels' that provides functions to register how models are to be used. Functions to register models are complimented with accessor functions to retrieve registered model information to aid in model fitting and error handling.
Last updated 6 months ago
4 stars 4.68 score 11 dependencies 37 dependents
corrr - Correlations in R
A tool for exploring correlations. It makes it possible to easily perform routine tasks when exploring correlation matrices such as ignoring the diagonal, focusing on the correlations of certain variables against others, or rearranging and visualizing the matrix in terms of the strength of the correlations.
Last updated 6 months ago
586 stars 7.84 score 46 dependencies 4 dependents
tidypredict - Run Predictions Inside the Database
It parses a fitted 'R' model object, and returns a formula in 'Tidy Eval' code that calculates the predictions. It works with several databases back-ends because it leverages 'dplyr' and 'dbplyr' for the final 'SQL' translation of the algorithm. It currently supports lm(), glm(), randomForest(), ranger(), earth(), xgb.Booster.complete(), cubist(), and ctree() models.
Last updated 6 months ago
dbplyrdplyrpurrrrlang
259 stars 6.05 score 26 dependencies 2 dependents
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 dependencies
spatialsample - Spatial Resampling Infrastructure
Functions and classes for spatial resampling to use with the 'rsample' package, such as spatial cross-validation (Brenning, 2012) <doi:10.1109/IGARSS.2012.6352393>. The scope of 'rsample' and 'spatialsample' is to provide the basic building blocks for creating and analyzing resamples of a spatial data set, but neither package includes functions for modeling or computing statistics. The resampled spatial data sets created by 'spatialsample' do not contain much overhead in memory.
Last updated 8 months ago
70 stars 3.89 score 57 dependencies 3 dependentsdesirability2 - 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 dependencies
multilevelmod - Model Wrappers for Multi-Level Models
Bindings for hierarchical regression models for use with the 'parsnip' package. Models include longitudinal generalized linear models (Liang and Zeger, 1986) <doi:10.1093/biomet/73.1.13>, and mixed-effect models (Pinheiro and Bates) <doi:10.1007/978-1-4419-0318-1_1>.
Last updated 8 months ago
74 stars 3.75 score 47 dependencies
applicable - A Compilation of Applicability Domain Methods
A modeling package compiling applicability domain methods in R. It combines different methods to measure the amount of extrapolation new samples can have from the training set. See Netzeva et al (2005) <doi:10.1177/026119290503300209> for an overview of applicability domains.
Last updated 1 years ago
45 stars 3.13 score 40 dependencies 1 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 84 dependencies