
Function reference
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recipes - recipes: A package for computing and preprocessing design matrices.
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recipe() - Create a recipe for preprocessing data
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formula(<recipe>) - Create a Formula from a Prepared Recipe
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print(<recipe>) - Print a Recipe
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summary(<recipe>) - Summarize a recipe
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prep() - Estimate a preprocessing recipe
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bake() - Apply a trained preprocessing recipe
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juice() - Extract transformed training set
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selections - Methods for selecting variables in step functions
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has_role()all_predictors()all_numeric_predictors()all_nominal_predictors()all_outcomes()has_type()all_numeric()all_nominal()current_info() - Role Selection
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add_role()update_role()remove_role() - Manually Alter Roles
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update_role_requirements() - Update role specific requirements
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get_case_weights()averages()medians()variances()correlations()covariances()pca_wts()are_weights_used() - Helpers for steps with case weights
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case_weights - Using case weights with recipes
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step_impute_bag()step_bagimpute()imp_vars() - Impute via bagged trees
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step_impute_knn()step_knnimpute() - Impute via k-nearest neighbors
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step_impute_linear() - Impute numeric variables via a linear model
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step_impute_lower()step_lowerimpute() - Impute numeric data below the threshold of measurement
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step_impute_mean()step_meanimpute() - Impute numeric data using the mean
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step_impute_median()step_medianimpute() - Impute numeric data using the median
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step_impute_mode()step_modeimpute() - Impute nominal data using the most common value
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step_impute_roll()step_rollimpute() - Impute numeric data using a rolling window statistic
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step_unknown() - Assign missing categories to "unknown"
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step_BoxCox() - Box-Cox Transformation for Non-Negative Data
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step_bs() - B-Spline Basis Functions
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step_harmonic() - Add sin and cos terms for harmonic analysis
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step_hyperbolic() - Hyperbolic Transformations
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step_inverse() - Inverse Transformation
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step_invlogit() - Inverse Logit Transformation
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step_log() - Logarithmic Transformation
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step_logit() - Logit Transformation
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step_mutate() - Add new variables using dplyr
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step_ns() - Natural Spline Basis Functions
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step_poly() - Orthogonal Polynomial Basis Functions
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step_relu() - Apply (Smoothed) Rectified Linear Transformation
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step_sqrt() - Square Root Transformation
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step_YeoJohnson() - Yeo-Johnson Transformation
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step_discretize() - Discretize Numeric Variables
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discretize()predict(<discretize>) - Discretize Numeric Variables
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step_cut() - Cut a numeric variable into a factor
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step_bin2factor() - Create a Factors from A Dummy Variable
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step_count() - Create Counts of Patterns using Regular Expressions
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step_date() - Date Feature Generator
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step_dummy() - Create traditional dummy variables
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step_dummy_extract() - Extract patterns from nominal data
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step_dummy_multi_choice() - Handle levels in multiple predictors together
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step_factor2string() - Convert Factors to Strings
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step_holiday() - Holiday Feature Generator
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step_indicate_na() - Create Missing Data Column Indicators
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step_integer() - Convert values to predefined integers
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step_novel() - Simple Value Assignments for Novel Factor Levels
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step_num2factor() - Convert Numbers to Factors
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step_ordinalscore() - Convert Ordinal Factors to Numeric Scores
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step_other() - Collapse Some Categorical Levels
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step_percentile() - Percentile Transformation
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step_regex() - Detect a regular expression
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step_relevel() - Relevel factors to a desired level
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step_string2factor() - Convert Strings to Factors
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step_time() - Time Feature Generator
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step_unknown() - Assign missing categories to "unknown"
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step_unorder() - Convert Ordered Factors to Unordered Factors
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step_interact() - Create Interaction Variables
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step_center() - Centering numeric data
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step_normalize() - Center and scale numeric data
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step_range() - Scaling Numeric Data to a Specific Range
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step_scale() - Scaling Numeric Data
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step_classdist() - Distances to Class Centroids
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step_depth() - Data Depths
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step_geodist() - Distance between two locations
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step_ica() - ICA Signal Extraction
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step_isomap() - Isomap Embedding
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step_kpca() - Kernel PCA Signal Extraction
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step_kpca_poly() - Polynomial Kernel PCA Signal Extraction
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step_kpca_rbf() - Radial Basis Function Kernel PCA Signal Extraction
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step_mutate_at() - Mutate multiple columns using dplyr
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step_nnmf() - Non-Negative Matrix Factorization Signal Extraction
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step_nnmf_sparse() - Non-Negative Matrix Factorization Signal Extraction with lasso Penalization
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step_pca() - PCA Signal Extraction
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step_pls() - Partial Least Squares Feature Extraction
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step_ratio()denom_vars() - Ratio Variable Creation
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step_spatialsign() - Spatial Sign Preprocessing
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step_corr() - High Correlation Filter
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step_filter_missing() - Missing Value Column Filter
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step_lincomb() - Linear Combination Filter
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step_nzv() - Near-Zero Variance Filter
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step_rm() - General Variable Filter
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step_select() - Select variables using dplyr
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step_zv() - Zero Variance Filter
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step_arrange() - Sort rows using dplyr
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step_filter() - Filter rows using dplyr
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step_lag() - Create a lagged predictor
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step_naomit() - Remove observations with missing values
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step_impute_roll()step_rollimpute() - Impute numeric data using a rolling window statistic
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step_sample() - Sample rows using dplyr
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step_shuffle() - Shuffle Variables
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step_slice() - Filter rows by position using dplyr
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step_intercept() - Add intercept (or constant) column
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step_profile() - Create a Profiling Version of a Data Set
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step_rename() - Rename variables by name using dplyr
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step_rename_at() - Rename multiple columns using dplyr
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step_window() - Moving Window Functions
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check_class() - Check Variable Class
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check_cols() - Check if all Columns are Present
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check_missing() - Check for Missing Values
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check_new_values() - Check for New Values
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check_range() - Check Range Consistency
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add_step()add_check() - Add a New Operation to the Current Recipe
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detect_step() - Detect if a particular step or check is used in a recipe
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fully_trained() - Check to see if a recipe is trained/prepared
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names0()dummy_names()dummy_extract_names() - Naming Tools
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prepper() - Wrapper function for preparing recipes within resampling
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recipes_eval_select() - Evaluate a selection with tidyselect semantics specific to recipes
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update(<step>) - Update a recipe step
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tidy(<step_BoxCox>)tidy(<step_YeoJohnson>)tidy(<step_arrange>)tidy(<step_bin2factor>)tidy(<step_bs>)tidy(<step_center>)tidy(<check_class>)tidy(<step_classdist>)tidy(<check_cols>)tidy(<step_corr>)tidy(<step_count>)tidy(<step_cut>)tidy(<step_date>)tidy(<step_depth>)tidy(<step_discretize>)tidy(<step_dummy>)tidy(<step_dummy_multi_choice>)tidy(<step_dummy_extract>)tidy(<step_factor2string>)tidy(<step_filter>)tidy(<step_filter_missing>)tidy(<step_geodist>)tidy(<step_harmonic>)tidy(<step_holiday>)tidy(<step_hyperbolic>)tidy(<step_ica>)tidy(<step_impute_bag>)tidy(<step_impute_knn>)tidy(<step_impute_linear>)tidy(<step_impute_lower>)tidy(<step_impute_mean>)tidy(<step_impute_median>)tidy(<step_impute_mode>)tidy(<step_impute_roll>)tidy(<step_integer>)tidy(<step_interact>)tidy(<step_inverse>)tidy(<step_invlogit>)tidy(<step_isomap>)tidy(<step_kpca>)tidy(<step_kpca_poly>)tidy(<step_kpca_rbf>)tidy(<step_lincomb>)tidy(<step_log>)tidy(<step_logit>)tidy(<check_missing>)tidy(<step_mutate>)tidy(<step_mutate_at>)tidy(<step_indicate_na>)tidy(<step_naomit>)tidy(<check_new_values>)tidy(<step_nnmf>)tidy(<step_nnmf_sparse>)tidy(<step_normalize>)tidy(<step_novel>)tidy(<step_ns>)tidy(<step_num2factor>)tidy(<step_nzv>)tidy(<step_ordinalscore>)tidy(<step_other>)tidy(<step_pca>)tidy(<step_percentile>)tidy(<step_pls>)tidy(<step_poly>)tidy(<step_profile>)tidy(<step_range>)tidy(<check_range>)tidy(<step_ratio>)tidy(<step_regex>)tidy(<step_relevel>)tidy(<step_relu>)tidy(<step_rename>)tidy(<step_rename_at>)tidy(<step_rm>)tidy(<step_sample>)tidy(<step_scale>)tidy(<step_select>)tidy(<step_shuffle>)tidy(<step_slice>)tidy(<step_spatialsign>)tidy(<step_sqrt>)tidy(<step_string2factor>)tidy(<recipe>)tidy(<step>)tidy(<check>)tidy(<step_time>)tidy(<step_unknown>)tidy(<step_unorder>)tidy(<step_window>)tidy(<step_zv>) - Tidy the Result of a Recipe