Functions required for parsnip-adjacent packages
Add a column of row numbers to a data frame
Developer functions for predictions via BART models
Bayesian additive regression trees (BART)
Calculations for inverse probability of censoring weights (IPCW)
Condense control object into strictly smaller control object
Helper functions to convert between formula and matrix interface
A wrapper function for conditional inference tree models
Data Set Characteristics Available when Fitting Models
Bayesian additive regression trees via dbarts
Flexible discriminant analysis via earth
Linear discriminant analysis via flexible discriminant analysis
Linear discriminant analysis via James-Stein-type shrinkage estimation
Linear discriminant analysis via regularization
Quadratic discriminant analysis via MASS
Quadratic discriminant analysis via regularization
Regularized discriminant analysis via klaR
Linear regression via generalized estimating equations (GEE)
Linear regression via generalized mixed models
Linear regression via generalized least squares
Linear regression via hierarchical Bayesian methods
Logistic regression via generalized estimating equations (GEE)
Logistic regression via hierarchical Bayesian methods
Multivariate adaptive regression splines (MARS) via earth
Poisson regression via generalized estimating equations (GEE)
Poisson regression via hierarchical Bayesian methods
Oblique random survival forests via aorsf
Linear support vector machines (SVMs) via LiblineaR
Linear support vector machines (SVMs) via kernlab
Polynomial support vector machines (SVMs) via kernlab
Radial basis function support vector machines (SVMs) via kernlab
Translate names of model tuning parameters
Extract elements of a parsnip model object
Internal functions that format predictions
Working with the parsnip model environment
Construct a single row summary "glance" of a model, fit, or other object
Fit a grouped binomial outcome from a data set with case weights
Helper functions for checking the penalty of glmnet models
Tools for models that predict on sub-models
Simple interface to MLP models via keras
Locate and show errors/warnings in engine-specific documentation
Multivariate adaptive regression splines (MARS)
Determine largest value of mtry from formula. This function potentially caps the value of 'mtry' based on a formula and data set. This is a safe approach for survival and/or multivariate models.
Formulas with special terms in tidymodels
Model predictions across many sub-models
Start an RStudio Addin that can write model specifications
Prepare data based on parsnip encoding information
Change elements of a model specification
Declare a computational engine and specific arguments
Set seed in R and TensorFlow at the same time
Display currently available engines for a model
Radial basis function support vector machines
Turn a parsnip model object into a tidy tibble
Resolve a Model Specification for a Computational Engine
A placeholder function for argument values