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| recipes-package | recipes: A package for computing and preprocessing design matrices. |
| add_check | Add a New Operation to the Current Recipe |
| add_role | Manually Alter Roles |
| add_step | Add a New Operation to the Current Recipe |
| all_nominal | Role Selection |
| all_numeric | Role Selection |
| all_outcomes | Role Selection |
| all_predictors | Role Selection |
| bake | Apply a Trained Data Recipe |
| bake.recipe | Apply a Trained Data Recipe |
| biomass | Biomass Data |
| check_cols | Check if all Columns are Present |
| check_missing | Check for Missing Values |
| check_range | Check Range Consistency |
| covers | Raw Cover Type Data |
| credit_data | Credit Data |
| current_info | Role Selection |
| denom_vars | Ratio Variable Creation |
| detect_step | Detect if a particular step or check is used in a recipe |
| discretize | Discretize Numeric Variables |
| discretize.default | Discretize Numeric Variables |
| discretize.numeric | Discretize Numeric Variables |
| dummy_names | Naming Tools |
| formula.recipe | Create a Formula from a Prepared Recipe |
| fully_trained | Check to see if a recipe is trained/prepared |
| has_role | Role Selection |
| has_type | Role Selection |
| imp_vars | Imputation via Bagged Trees |
| juice | Extract Finalized Training Set |
| names0 | Naming Tools |
| okc | OkCupid Data |
| predict.discretize | Discretize Numeric Variables |
| prep | Train a Data Recipe |
| prep.recipe | Train a Data Recipe |
| prepper | Wrapper function for preparing recipes within resampling |
| print.recipe | Print a Recipe |
| recipe | Create a Recipe for Preprocessing Data |
| recipe.data.frame | Create a Recipe for Preprocessing Data |
| recipe.default | Create a Recipe for Preprocessing Data |
| recipe.formula | Create a Recipe for Preprocessing Data |
| recipe.matrix | Create a Recipe for Preprocessing Data |
| recipes | recipes: A package for computing and preprocessing design matrices. |
| roles | Manually Alter Roles |
| selections | Methods for Select Variables in Step Functions |
| Smithsonian | Smithsonian Museums |
| step_arrange | Sort rows using dplyr |
| step_bagimpute | Imputation via Bagged Trees |
| step_bin2factor | Create a Factors from A Dummy Variable |
| step_BoxCox | Box-Cox Transformation for Non-Negative Data |
| step_bs | B-Spline Basis Functions |
| step_center | Centering Numeric Data |
| step_classdist | Distances to Class Centroids |
| step_corr | High Correlation Filter |
| step_count | Create Counts of Patterns using Regular Expressions |
| step_date | Date Feature Generator |
| step_depth | Data Depths |
| step_discretize | Discretize Numeric Variables |
| step_downsample | Down-Sample a Data Set Based on a Factor Variable |
| step_dummy | Dummy Variables Creation |
| step_factor2string | Convert Factors to Strings |
| step_filter | Filter rows using dplyr |
| step_geodist | Distance between two locations |
| step_holiday | Holiday Feature Generator |
| step_hyperbolic | Hyperbolic Transformations |
| step_ica | ICA Signal Extraction |
| step_integer | Convert values to predefined integers |
| step_interact | Create Interaction Variables |
| step_intercept | Add intercept (or constant) column |
| step_inverse | Inverse Transformation |
| step_invlogit | Inverse Logit Transformation |
| step_isomap | Isomap Embedding |
| step_knnimpute | Imputation via K-Nearest Neighbors |
| step_kpca | Kernel PCA Signal Extraction |
| step_lag | Create a lagged predictor |
| step_lincomb | Linear Combination Filter |
| step_log | Logarithmic Transformation |
| step_logit | Logit Transformation |
| step_lowerimpute | Impute Numeric Data Below the Threshold of Measurement |
| step_meanimpute | Impute Numeric Data Using the Mean |
| step_medianimpute | Impute Numeric Data Using the Median |
| step_modeimpute | Impute Nominal Data Using the Most Common Value |
| step_mutate | Add new variables using 'mutate' |
| step_naomit | Remove observations with missing values |
| step_nnmf | NNMF Signal Extraction |
| step_novel | Simple Value Assignments for Novel Factor Levels |
| step_ns | Nature Spline Basis Functions |
| step_num2factor | Convert Numbers to Factors |
| step_nzv | Near-Zero Variance Filter |
| step_ordinalscore | Convert Ordinal Factors to Numeric Scores |
| step_other | Collapse Some Categorical Levels |
| step_pca | PCA Signal Extraction |
| step_pls | Partial Least Squares Feature Extraction |
| step_poly | Orthogonal Polynomial Basis Functions |
| step_profile | Create a Profiling Version of a Data Set |
| step_range | Scaling Numeric Data to a Specific Range |
| step_ratio | Ratio Variable Creation |
| step_regex | Create Dummy Variables using Regular Expressions |
| step_relu | Apply (Smoothed) Rectified Linear Transformation |
| step_rm | General Variable Filter |
| step_rollimpute | Impute Numeric Data Using a Rolling Window Statistic |
| step_sample | Sample rows using dplyr |
| step_scale | Scaling Numeric Data |
| step_shuffle | Shuffle Variables |
| step_slice | Filter rows by position using dplyr |
| step_spatialsign | Spatial Sign Preprocessing |
| step_sqrt | Square Root Transformation |
| step_string2factor | Convert Strings to Factors |
| step_unorder | Convert Ordered Factors to Unordered Factors |
| step_upsample | Up-Sample a Data Set Based on a Factor Variable |
| step_window | Moving Window Functions |
| step_YeoJohnson | Yeo-Johnson Transformation |
| step_zv | Zero Variance Filter |
| summary.recipe | Summarize a Recipe |
| terms_select | Select Terms in a Step Function. |
| tidy.check | Tidy the Result of a Recipe |
| tidy.check_cols | Check if all Columns are Present |
| tidy.check_missing | Check for Missing Values |
| tidy.check_range | Check Range Consistency |
| tidy.recipe | Tidy the Result of a Recipe |
| tidy.step | Tidy the Result of a Recipe |
| tidy.step_arrange | Sort rows using dplyr |
| tidy.step_bagimpute | Imputation via Bagged Trees |
| tidy.step_bin2factor | Create a Factors from A Dummy Variable |
| tidy.step_BoxCox | Box-Cox Transformation for Non-Negative Data |
| tidy.step_bs | B-Spline Basis Functions |
| tidy.step_center | Centering Numeric Data |
| tidy.step_classdist | Distances to Class Centroids |
| tidy.step_corr | High Correlation Filter |
| tidy.step_count | Create Counts of Patterns using Regular Expressions |
| tidy.step_date | Date Feature Generator |
| tidy.step_depth | Data Depths |
| tidy.step_discretize | Discretize Numeric Variables |
| tidy.step_downsample | Down-Sample a Data Set Based on a Factor Variable |
| tidy.step_dummy | Dummy Variables Creation |
| tidy.step_factor2string | Convert Factors to Strings |
| tidy.step_filter | Filter rows using dplyr |
| tidy.step_geodist | Distance between two locations |
| tidy.step_holiday | Holiday Feature Generator |
| tidy.step_hyperbolic | Hyperbolic Transformations |
| tidy.step_ica | ICA Signal Extraction |
| tidy.step_integer | Convert values to predefined integers |
| tidy.step_interact | Create Interaction Variables |
| tidy.step_inverse | Inverse Transformation |
| tidy.step_invlogit | Inverse Logit Transformation |
| tidy.step_isomap | Isomap Embedding |
| tidy.step_knnimpute | Imputation via K-Nearest Neighbors |
| tidy.step_kpca | Kernel PCA Signal Extraction |
| tidy.step_lincomb | Linear Combination Filter |
| tidy.step_log | Logarithmic Transformation |
| tidy.step_logit | Logit Transformation |
| tidy.step_lowerimpute | Impute Numeric Data Below the Threshold of Measurement |
| tidy.step_meanimpute | Impute Numeric Data Using the Mean |
| tidy.step_medianimpute | Impute Numeric Data Using the Median |
| tidy.step_modeimpute | Impute Nominal Data Using the Most Common Value |
| tidy.step_mutate | Add new variables using 'mutate' |
| tidy.step_naomit | Remove observations with missing values |
| tidy.step_nnmf | NNMF Signal Extraction |
| tidy.step_novel | Simple Value Assignments for Novel Factor Levels |
| tidy.step_ns | Nature Spline Basis Functions |
| tidy.step_num2factor | Convert Numbers to Factors |
| tidy.step_nzv | Near-Zero Variance Filter |
| tidy.step_ordinalscore | Convert Ordinal Factors to Numeric Scores |
| tidy.step_other | Collapse Some Categorical Levels |
| tidy.step_pca | PCA Signal Extraction |
| tidy.step_pls | Partial Least Squares Feature Extraction |
| tidy.step_poly | Orthogonal Polynomial Basis Functions |
| tidy.step_profile | Create a Profiling Version of a Data Set |
| tidy.step_range | Scaling Numeric Data to a Specific Range |
| tidy.step_ratio | Ratio Variable Creation |
| tidy.step_regex | Create Dummy Variables using Regular Expressions |
| tidy.step_relu | Apply (Smoothed) Rectified Linear Transformation |
| tidy.step_rm | General Variable Filter |
| tidy.step_rollimpute | Impute Numeric Data Using a Rolling Window Statistic |
| tidy.step_sample | Sample rows using dplyr |
| tidy.step_scale | Scaling Numeric Data |
| tidy.step_shuffle | Shuffle Variables |
| tidy.step_slice | Filter rows by position using dplyr |
| tidy.step_spatialsign | Spatial Sign Preprocessing |
| tidy.step_sqrt | Square Root Transformation |
| tidy.step_string2factor | Convert Strings to Factors |
| tidy.step_unorder | Convert Ordered Factors to Unordered Factors |
| tidy.step_upsample | Up-Sample a Data Set Based on a Factor Variable |
| tidy.step_window | Moving Window Functions |
| tidy.step_YeoJohnson | Yeo-Johnson Transformation |
| tidy.step_zv | Zero Variance Filter |
| update_role | Manually Alter Roles |