parsnip: A Common API to Modeling and Analysis Functions

Canonical link: https://rscholar.com/pkg/parsnip

Max Kuhn • Davis Vaughan • Emil Hvitfeldt • Posit Software • View description and downloads View dependencies GitHub project

2018 Published

1.2.1 Version

0 Citations

4 Authors

Referenced by ⇅	Year
kmscv: K-Means Based Stratified Resampling (Version 0.1.0)	2022
modeltime.gluonts: "GluonTS" Deep Learning (Version 0.1.0)	2020
modeltime.h2o: Modeltime "H2O" Machine Learning (Version 0.1.1)	2021
viruslearner: Ensemble Learning for HIV-Related Metrics (Version 0.0.2)	2024
LorenzRegression: Lorenz and Penalized Lorenz Regressions (Version 2.1.0)	2023
MLDataR: Collection of Machine Learning Datasets for Supervised Machine Learning (Version 1.0.1)	2022
MachineShop: Machine Learning Models and Tools (Version 3.8.0)	2018
SSLR: Semi-Supervised Classification, Regression and Clustering Methods (Version 0.9.3.3)	2020
additive: Bindings for Additive TidyModels (Version 1.0.1)	2021
agua: 'tidymodels' Integration with 'h2o' (Version 0.1.4)	2022
autostats: Auto Stats (Version 0.4.1)	2021
baguette: Efficient Model Functions for Bagging (Version 1.0.2)	2020
bayesian: Bindings for Bayesian TidyModels (Version 1.0.1)	2021
bonsai: Model Wrappers for Tree-Based Models (Version 0.3.1)	2022
broom.helpers: Helpers for Model Coefficients Tibbles (Version 1.17.0)	2020
bundle: Serialize Model Objects with a Consistent Interface (Version 0.1.1)	2022
butcher: Model Butcher (Version 0.3.4)	2019
card: Cardiovascular Applications in Research Data (Version 0.1.1)	2020
censored: 'parsnip' Engines for Survival Models (Version 0.3.2)	2022
coefplot: Plots Coefficients from Fitted Models (Version 1.2.8)	2011
condvis2: Interactive Conditional Visualization for Supervised and Unsupervised Models in Shiny (Version 0.1.2)	2019
cuda.ml: R Interface for the RAPIDS cuML Suite of Libraries (Version 0.3.2)	2021
discrim: Model Wrappers for Discriminant Analysis (Version 1.0.1)	2019
easyalluvial: Generate Alluvial Plots with a Single Line of Code (Version 0.3.2)	2018
easysurv: Simplify Survival Data Analysis and Model Fitting (Version 2.0.1)	2024
effectsize: Indices of Effect Size (Version 0.8.9)	2019
finetune: Additional Functions for Model Tuning (Version 1.2.0)	2020
finnts: Microsoft Finance Time Series Forecasting Framework (Version 0.5.0)	2022
forestControl: Approximate False Positive Rate Control in Selection Frequency for Random Forest (Version 0.2.2)	2018
ggeffects: Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs (Version 1.7.2)	2017
gtsummary: Presentation-Ready Data Summary and Analytic Result Tables (Version 2.0.3)	2019
healthyR.ai: The Machine Learning and AI Modeling Companion to 'healthyR' (Version 0.1.0)	2021
healthyR.ts: The Time Series Modeling Companion to 'healthyR' (Version 0.3.1)	2021
imputeGeneric: Ease the Implementation of Imputation Methods (Version 0.1.0)	2022
infer: Tidy Statistical Inference (Version 1.0.7)	2018
insight: Easy Access to Model Information for Various Model Objects (Version 0.20.5)	2019
lnmixsurv: Bayesian Mixture Log-Normal Survival Model (Version 3.1.6)	2024
marginaleffects: Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests (Version 0.23.0)	2021
mmrm: Mixed Models for Repeated Measures (Version 0.3.14)	2022
modeltime.resample: Resampling Tools for Time Series Forecasting (Version 0.2.3)	2020
modeltime: The Tidymodels Extension for Time Series Modeling (Version 1.3.1)	2020
multilevelmod: Model Wrappers for Multi-Level Models (Version 1.0.0)	2022
nestedmodels: Tidy Modelling for Nested Data (Version 1.1.0)	2022
offsetreg: An Extension of 'Tidymodels' Supporting Offset Terms (Version 1.1.0)	2024
orbital: Predict with 'tidymodels' Workflows in Databases (Version 0.2.0)	2024
plsmod: Model Wrappers for Projection Methods (Version 1.0.0)	2020
poissonreg: Model Wrappers for Poisson Regression (Version 1.0.1)	2020
ppsr: Predictive Power Score (Version 0.0.5)	2021
probably: Tools for Post-Processing Predicted Values (Version 1.0.3)	2018
recipes: Preprocessing and Feature Engineering Steps for Modeling (Version 1.1.0)	2017
rules: Model Wrappers for Rule-Based Models (Version 1.0.2)	2020
shinymodels: Interactive Assessments of Models (Version 0.1.1)	2021
sparklyr: R Interface to Apache Spark (Version 1.8.6)	2016
stacks: Tidy Model Stacking (Version 1.0.5)	2020
survex: Explainable Machine Learning in Survival Analysis (Version 1.2.0)	2022
tabnet: Fit 'TabNet' Models for Classification and Regression (Version 0.6.0)	2021
text: Analyses of Text using Transformers Models from HuggingFace, Natural Language Processing and Machine Learning (Version 1.2.3)	2020
tidyAML: Automatic Machine Learning with 'tidymodels' (Version 0.0.5)	2023
tidyclust: A Common API to Clustering (Version 0.2.3)	2022
tidydann: Add the 'dann' Model and the 'sub_dann' Model to the Tidymodels Ecosystem (Version 1.0.0)	2023
tidymodels: Easily Install and Load the 'Tidymodels' Packages (Version 1.2.0)	2018
tidyposterior: Bayesian Analysis to Compare Models using Resampling Statistics (Version 1.0.1)	2017
tidypredict: Run Predictions Inside the Database (Version 0.5)	2018
tidysdm: Species Distribution Models with Tidymodels (Version 0.9.5)	2023
timetk: A Tool Kit for Working with Time Series (Version 2.9.0)	2017
tune: Tidy Tuning Tools (Version 1.2.1)	2020
vetiver: Version, Share, Deploy, and Monitor Models (Version 0.2.5)	2021
viraldomain: Applicability Domain Methods of Viral Load and CD4 Lymphocytes (Version 0.0.6)	2023
vip: Variable Importance Plots (Version 0.4.1)	2018
viralmodels: Viral Load and CD4 Lymphocytes Regression Models (Version 1.3.1)	2023
viralx: Explainers for Regression Models in HIV Research (Version 1.3.0)	2023
workflows: Modeling Workflows (Version 1.1.4)	2019
workflowsets: Create a Collection of 'tidymodels' Workflows (Version 1.1.0)	2021

RPKG Scholar presents a tabulation of an author's contribution in the development of R packages stored in the Comprehensive R Archive Network (CRAN). Within this site, we consider package dependencies (suggests,imports,depends,enhances) as citations because we believe that using one's package to develop another is tantamount to citing the author of the package being imported, suggested or enhanced.