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    },
    {
      "version": "1.3.2",
      "date": "2026-04-02"
    },
    {
      "version": "1.3.3",
      "date": "2026-05-30"
    }
  ],
  "_exports": [
    ".get_data_types",
    ".recipes_estimate_sparsity",
    ".recipes_preserve_sparsity",
    ".recipes_toggle_sparse_args",
    "%>%",
    "add_check",
    "add_role",
    "add_step",
    "all_date",
    "all_date_predictors",
    "all_datetime",
    "all_datetime_predictors",
    "all_double",
    "all_double_predictors",
    "all_factor",
    "all_factor_predictors",
    "all_integer",
    "all_integer_predictors",
    "all_logical",
    "all_logical_predictors",
    "all_nominal",
    "all_nominal_predictors",
    "all_numeric",
    "all_numeric_predictors",
    "all_ordered",
    "all_ordered_predictors",
    "all_outcomes",
    "all_predictors",
    "all_string",
    "all_string_predictors",
    "all_unordered",
    "all_unordered_predictors",
    "are_weights_used",
    "averages",
    "bake",
    "check",
    "check_class",
    "check_cols",
    "check_missing",
    "check_name",
    "check_new_data",
    "check_new_values",
    "check_options",
    "check_range",
    "check_type",
    "correlations",
    "covariances",
    "current_info",
    "denom_vars",
    "detect_step",
    "discretize",
    "dummy_extract_names",
    "dummy_names",
    "ellipse_check",
    "estimate_yj",
    "extract_fit_time",
    "extract_parameter_dials",
    "extract_parameter_set_dials",
    "fixed",
    "format_ch_vec",
    "format_selectors",
    "frequency_weights",
    "fully_trained",
    "get_case_weights",
    "get_keep_original_cols",
    "has_role",
    "has_type",
    "imp_vars",
    "importance_weights",
    "is_trained",
    "juice",
    "medians",
    "names0",
    "pca_wts",
    "prep",
    "prepare",
    "prepper",
    "print_step",
    "printer",
    "prof",
    "rand_id",
    "recipe",
    "recipes_argument_select",
    "recipes_eval_select",
    "recipes_extension_check",
    "recipes_names_outcomes",
    "recipes_names_predictors",
    "recipes_pkg_check",
    "recipes_ptype",
    "recipes_ptype_validate",
    "recipes_remove_cols",
    "remove_original_cols",
    "remove_role",
    "required_pkgs",
    "sel2char",
    "step",
    "step_arrange",
    "step_bagimpute",
    "step_bin2factor",
    "step_BoxCox",
    "step_bs",
    "step_center",
    "step_classdist",
    "step_classdist_shrunken",
    "step_corr",
    "step_count",
    "step_cut",
    "step_date",
    "step_depth",
    "step_discretize",
    "step_dummy",
    "step_dummy_extract",
    "step_dummy_multi_choice",
    "step_factor2string",
    "step_filter",
    "step_filter_missing",
    "step_geodist",
    "step_harmonic",
    "step_holiday",
    "step_hyperbolic",
    "step_ica",
    "step_impute_bag",
    "step_impute_knn",
    "step_impute_linear",
    "step_impute_lower",
    "step_impute_mean",
    "step_impute_median",
    "step_impute_mode",
    "step_impute_roll",
    "step_indicate_na",
    "step_integer",
    "step_interact",
    "step_intercept",
    "step_inverse",
    "step_invlogit",
    "step_isomap",
    "step_knnimpute",
    "step_kpca",
    "step_kpca_poly",
    "step_kpca_rbf",
    "step_lag",
    "step_lincomb",
    "step_log",
    "step_logit",
    "step_lowerimpute",
    "step_meanimpute",
    "step_medianimpute",
    "step_modeimpute",
    "step_mutate",
    "step_mutate_at",
    "step_naomit",
    "step_nnmf",
    "step_nnmf_sparse",
    "step_normalize",
    "step_novel",
    "step_ns",
    "step_num2factor",
    "step_nzv",
    "step_ordinalscore",
    "step_other",
    "step_pca",
    "step_percentile",
    "step_pls",
    "step_poly",
    "step_poly_bernstein",
    "step_profile",
    "step_range",
    "step_ratio",
    "step_regex",
    "step_relevel",
    "step_relu",
    "step_rename",
    "step_rename_at",
    "step_rm",
    "step_rollimpute",
    "step_sample",
    "step_scale",
    "step_select",
    "step_shuffle",
    "step_slice",
    "step_spatialsign",
    "step_spline_b",
    "step_spline_convex",
    "step_spline_monotone",
    "step_spline_natural",
    "step_spline_nonnegative",
    "step_sqrt",
    "step_string2factor",
    "step_time",
    "step_unknown",
    "step_unorder",
    "step_window",
    "step_YeoJohnson",
    "step_zv",
    "terms_select",
    "tidy",
    "tunable",
    "tune_args",
    "update",
    "update_role",
    "update_role_requirements",
    "variances",
    "yj_transform"
  ],
  "_help": [
    {
      "page": "get_data_types",
      "title": "Get types for use in recipes",
      "topics": [
        ".get_data_types",
        ".get_data_types.character",
        ".get_data_types.Date",
        ".get_data_types.default",
        ".get_data_types.double",
        ".get_data_types.factor",
        ".get_data_types.hardhat_case_weights",
        ".get_data_types.integer",
        ".get_data_types.list",
        ".get_data_types.logical",
        ".get_data_types.numeric",
        ".get_data_types.ordered",
        ".get_data_types.POSIXct",
        ".get_data_types.Surv",
        ".get_data_types.textrecipes_tokenlist"
      ]
    },
    {
      "page": "add_step",
      "title": "Add a New Operation to the Current Recipe",
      "topics": [
        "add_check",
        "add_step"
      ]
    },
    {
      "page": "bake",
      "title": "Apply a trained preprocessing recipe",
      "topics": [
        "bake",
        "bake.recipe"
      ]
    },
    {
      "page": "case_weights",
      "title": "Using case weights with recipes",
      "topics": [
        "case_weights"
      ]
    },
    {
      "page": "case-weight-helpers",
      "title": "Helpers for steps with case weights",
      "topics": [
        "are_weights_used",
        "averages",
        "case-weight-helpers",
        "correlations",
        "covariances",
        "get_case_weights",
        "medians",
        "pca_wts",
        "variances"
      ]
    },
    {
      "page": "check_class",
      "title": "Check variable class",
      "concept": [
        "checks"
      ],
      "topics": [
        "check_class"
      ]
    },
    {
      "page": "check_cols",
      "title": "Check if all columns are present",
      "concept": [
        "checks"
      ],
      "topics": [
        "check_cols"
      ]
    },
    {
      "page": "check_missing",
      "title": "Check for missing values",
      "concept": [
        "checks"
      ],
      "topics": [
        "check_missing"
      ]
    },
    {
      "page": "check_new_values",
      "title": "Check for new values",
      "concept": [
        "checks"
      ],
      "topics": [
        "check_new_values"
      ]
    },
    {
      "page": "check_range",
      "title": "Check range consistency",
      "concept": [
        "checks"
      ],
      "topics": [
        "check_range"
      ]
    },
    {
      "page": "detect_step",
      "title": "Detect if a particular step or check is used in a recipe",
      "topics": [
        "detect_step"
      ]
    },
    {
      "page": "developer_functions",
      "title": "Developer functions for creating recipes steps",
      "topics": [
        "developer_functions"
      ]
    },
    {
      "page": "discretize",
      "title": "Discretize Numeric Variables",
      "topics": [
        "discretize",
        "discretize.default",
        "discretize.numeric",
        "predict.discretize"
      ]
    },
    {
      "page": "formula.recipe",
      "title": "Create a formula from a prepared recipe",
      "topics": [
        "formula.recipe"
      ]
    },
    {
      "page": "fully_trained",
      "title": "Check to see if a recipe is trained/prepared",
      "topics": [
        "fully_trained"
      ]
    },
    {
      "page": "has_role",
      "title": "Role Selection",
      "topics": [
        "all_date",
        "all_datetime",
        "all_datetime_predictors",
        "all_date_predictors",
        "all_double",
        "all_double_predictors",
        "all_factor",
        "all_factor_predictors",
        "all_integer",
        "all_integer_predictors",
        "all_logical",
        "all_logical_predictors",
        "all_nominal",
        "all_nominal_predictors",
        "all_numeric",
        "all_numeric_predictors",
        "all_ordered",
        "all_ordered_predictors",
        "all_outcomes",
        "all_predictors",
        "all_string",
        "all_string_predictors",
        "all_unordered",
        "all_unordered_predictors",
        "current_info",
        "has_role",
        "has_type"
      ]
    },
    {
      "page": "juice",
      "title": "Extract transformed training set",
      "topics": [
        "juice"
      ]
    },
    {
      "page": "names0",
      "title": "Naming Tools",
      "topics": [
        "dummy_extract_names",
        "dummy_names",
        "names0"
      ]
    },
    {
      "page": "prep",
      "title": "Estimate a preprocessing recipe",
      "topics": [
        "prep",
        "prep.recipe"
      ]
    },
    {
      "page": "prepper",
      "title": "Wrapper function for preparing recipes within resampling",
      "topics": [
        "prepper"
      ]
    },
    {
      "page": "print.recipe",
      "title": "Print a Recipe",
      "topics": [
        "print.recipe"
      ]
    },
    {
      "page": "recipe",
      "title": "Create a recipe for preprocessing data",
      "topics": [
        "recipe",
        "recipe.data.frame",
        "recipe.default",
        "recipe.formula",
        "recipe.matrix"
      ]
    },
    {
      "page": "recipes_argument_select",
      "title": "Evaluate a selection with tidyselect semantics for arguments",
      "topics": [
        "recipes_argument_select"
      ]
    },
    {
      "page": "recipes_eval_select",
      "title": "Evaluate a selection with tidyselect semantics specific to recipes",
      "topics": [
        "recipes_eval_select"
      ]
    },
    {
      "page": "recipes_extension_check",
      "title": "Checks that steps have all S3 methods",
      "topics": [
        "recipes_extension_check"
      ]
    },
    {
      "page": "roles",
      "title": "Manually alter roles",
      "topics": [
        "add_role",
        "remove_role",
        "roles",
        "update_role"
      ]
    },
    {
      "page": "selections",
      "title": "Methods for selecting variables in step functions",
      "topics": [
        "selection",
        "selections"
      ]
    },
    {
      "page": "sparse_data",
      "title": "Using sparse data with recipes",
      "topics": [
        "sparse_data"
      ]
    },
    {
      "page": "step_arrange",
      "title": "Sort rows using dplyr",
      "concept": [
        "dplyr steps",
        "row operation steps"
      ],
      "topics": [
        "step_arrange"
      ]
    },
    {
      "page": "step_bin2factor",
      "title": "Create a factors from A dummy variable",
      "concept": [
        "dummy variable and encoding steps"
      ],
      "topics": [
        "step_bin2factor"
      ]
    },
    {
      "page": "step_BoxCox",
      "title": "Box-Cox transformation for non-negative data",
      "concept": [
        "individual transformation steps"
      ],
      "topics": [
        "step_BoxCox"
      ]
    },
    {
      "page": "step_bs",
      "title": "B-spline basis functions",
      "concept": [
        "individual transformation steps"
      ],
      "topics": [
        "step_bs"
      ]
    },
    {
      "page": "step_center",
      "title": "Centering numeric data",
      "concept": [
        "normalization steps"
      ],
      "topics": [
        "step_center"
      ]
    },
    {
      "page": "step_classdist",
      "title": "Distances to class centroids",
      "concept": [
        "multivariate transformation steps"
      ],
      "topics": [
        "step_classdist"
      ]
    },
    {
      "page": "step_classdist_shrunken",
      "title": "Compute shrunken centroid distances for classification models",
      "concept": [
        "multivariate transformation steps"
      ],
      "topics": [
        "step_classdist_shrunken"
      ]
    },
    {
      "page": "step_corr",
      "title": "High correlation filter",
      "concept": [
        "variable filter steps"
      ],
      "topics": [
        "step_corr"
      ]
    },
    {
      "page": "step_count",
      "title": "Create counts of patterns using regular expressions",
      "concept": [
        "dummy variable and encoding steps"
      ],
      "topics": [
        "step_count"
      ]
    },
    {
      "page": "step_cut",
      "title": "Cut a numeric variable into a factor",
      "concept": [
        "discretization steps"
      ],
      "topics": [
        "step_cut"
      ]
    },
    {
      "page": "step_date",
      "title": "Date feature generator",
      "concept": [
        "dummy variable and encoding steps"
      ],
      "topics": [
        "step_date"
      ]
    },
    {
      "page": "step_depth",
      "title": "Data depths",
      "concept": [
        "multivariate transformation steps"
      ],
      "topics": [
        "step_depth"
      ]
    },
    {
      "page": "step_discretize",
      "title": "Discretize Numeric Variables",
      "concept": [
        "discretization steps"
      ],
      "topics": [
        "step_discretize"
      ]
    },
    {
      "page": "step_dummy",
      "title": "Create traditional dummy variables",
      "concept": [
        "dummy variable and encoding steps"
      ],
      "topics": [
        "step_dummy"
      ]
    },
    {
      "page": "step_dummy_extract",
      "title": "Extract patterns from nominal data",
      "concept": [
        "dummy variable and encoding steps"
      ],
      "topics": [
        "step_dummy_extract"
      ]
    },
    {
      "page": "step_dummy_multi_choice",
      "title": "Handle levels in multiple predictors together",
      "concept": [
        "dummy variable and encoding steps"
      ],
      "topics": [
        "step_dummy_multi_choice"
      ]
    },
    {
      "page": "step_factor2string",
      "title": "Convert factors to strings",
      "concept": [
        "dummy variable and encoding steps"
      ],
      "topics": [
        "step_factor2string"
      ]
    },
    {
      "page": "step_filter",
      "title": "Filter rows using dplyr",
      "concept": [
        "dplyr steps",
        "row operation steps"
      ],
      "topics": [
        "step_filter"
      ]
    },
    {
      "page": "step_filter_missing",
      "title": "Missing value column filter",
      "concept": [
        "variable filter steps"
      ],
      "topics": [
        "step_filter_missing"
      ]
    },
    {
      "page": "step_geodist",
      "title": "Distance between two locations",
      "concept": [
        "multivariate transformation steps"
      ],
      "topics": [
        "step_geodist"
      ]
    },
    {
      "page": "step_harmonic",
      "title": "Add sin and cos terms for harmonic analysis",
      "concept": [
        "individual transformation steps"
      ],
      "topics": [
        "step_harmonic"
      ]
    },
    {
      "page": "step_holiday",
      "title": "Holiday feature generator",
      "concept": [
        "dummy variable and encoding steps"
      ],
      "topics": [
        "step_holiday"
      ]
    },
    {
      "page": "step_hyperbolic",
      "title": "Hyperbolic transformations",
      "concept": [
        "individual transformation steps"
      ],
      "topics": [
        "step_hyperbolic"
      ]
    },
    {
      "page": "step_ica",
      "title": "ICA signal extraction",
      "concept": [
        "multivariate transformation steps"
      ],
      "topics": [
        "step_ica"
      ]
    },
    {
      "page": "step_impute_bag",
      "title": "Impute via bagged trees",
      "concept": [
        "imputation steps"
      ],
      "topics": [
        "imp_vars",
        "step_impute_bag"
      ]
    },
    {
      "page": "step_impute_knn",
      "title": "Impute via k-nearest neighbors",
      "concept": [
        "imputation steps"
      ],
      "topics": [
        "step_impute_knn"
      ]
    },
    {
      "page": "step_impute_linear",
      "title": "Impute numeric variables via a linear model",
      "concept": [
        "imputation steps"
      ],
      "topics": [
        "step_impute_linear"
      ]
    },
    {
      "page": "step_impute_lower",
      "title": "Impute numeric data below the threshold of measurement",
      "concept": [
        "imputation steps"
      ],
      "topics": [
        "step_impute_lower"
      ]
    },
    {
      "page": "step_impute_mean",
      "title": "Impute numeric data using the mean",
      "concept": [
        "imputation steps"
      ],
      "topics": [
        "step_impute_mean"
      ]
    },
    {
      "page": "step_impute_median",
      "title": "Impute numeric data using the median",
      "concept": [
        "imputation steps"
      ],
      "topics": [
        "step_impute_median"
      ]
    },
    {
      "page": "step_impute_mode",
      "title": "Impute nominal data using the most common value",
      "concept": [
        "imputation steps"
      ],
      "topics": [
        "step_impute_mode"
      ]
    },
    {
      "page": "step_impute_roll",
      "title": "Impute numeric data using a rolling window statistic",
      "concept": [
        "imputation steps",
        "row operation steps"
      ],
      "topics": [
        "step_impute_roll"
      ]
    },
    {
      "page": "step_indicate_na",
      "title": "Create missing data column indicators",
      "concept": [
        "dummy variable and encoding steps"
      ],
      "topics": [
        "step_indicate_na"
      ]
    },
    {
      "page": "step_integer",
      "title": "Convert values to predefined integers",
      "concept": [
        "dummy variable and encoding steps"
      ],
      "topics": [
        "step_integer"
      ]
    },
    {
      "page": "step_interact",
      "title": "Create interaction variables",
      "topics": [
        "step_interact"
      ]
    },
    {
      "page": "step_intercept",
      "title": "Add intercept (or constant) column",
      "topics": [
        "step_intercept"
      ]
    },
    {
      "page": "step_inverse",
      "title": "Inverse transformation",
      "concept": [
        "individual transformation steps"
      ],
      "topics": [
        "step_inverse"
      ]
    },
    {
      "page": "step_invlogit",
      "title": "Inverse logit transformation",
      "concept": [
        "individual transformation steps"
      ],
      "topics": [
        "step_invlogit"
      ]
    },
    {
      "page": "step_isomap",
      "title": "Isomap embedding",
      "concept": [
        "multivariate transformation steps"
      ],
      "topics": [
        "step_isomap"
      ]
    },
    {
      "page": "step_kpca",
      "title": "Kernel PCA signal extraction",
      "concept": [
        "multivariate transformation steps"
      ],
      "topics": [
        "step_kpca"
      ]
    },
    {
      "page": "step_kpca_poly",
      "title": "Polynomial kernel PCA signal extraction",
      "concept": [
        "multivariate transformation steps"
      ],
      "topics": [
        "step_kpca_poly"
      ]
    },
    {
      "page": "step_kpca_rbf",
      "title": "Radial basis function kernel PCA signal extraction",
      "concept": [
        "multivariate transformation steps"
      ],
      "topics": [
        "step_kpca_rbf"
      ]
    },
    {
      "page": "step_lag",
      "title": "Create a lagged predictor",
      "concept": [
        "row operation steps"
      ],
      "topics": [
        "step_lag"
      ]
    },
    {
      "page": "step_lincomb",
      "title": "Linear combination filter",
      "concept": [
        "variable filter steps"
      ],
      "topics": [
        "step_lincomb"
      ]
    },
    {
      "page": "step_log",
      "title": "Logarithmic transformation",
      "concept": [
        "individual transformation steps"
      ],
      "topics": [
        "step_log"
      ]
    },
    {
      "page": "step_logit",
      "title": "Logit transformation",
      "concept": [
        "individual transformation steps"
      ],
      "topics": [
        "step_logit"
      ]
    },
    {
      "page": "step_mutate",
      "title": "Add new variables using dplyr",
      "concept": [
        "dplyr steps",
        "individual transformation steps"
      ],
      "topics": [
        "step_mutate"
      ]
    },
    {
      "page": "step_mutate_at",
      "title": "Mutate multiple columns using dplyr",
      "concept": [
        "dplyr steps",
        "multivariate transformation steps"
      ],
      "topics": [
        "step_mutate_at"
      ]
    },
    {
      "page": "step_naomit",
      "title": "Remove observations with missing values",
      "concept": [
        "row operation steps"
      ],
      "topics": [
        "step_naomit"
      ]
    },
    {
      "page": "step_nnmf_sparse",
      "title": "Non-negative matrix factorization signal extraction with lasso penalization",
      "concept": [
        "multivariate transformation steps"
      ],
      "topics": [
        "step_nnmf_sparse"
      ]
    },
    {
      "page": "step_normalize",
      "title": "Center and scale numeric data",
      "concept": [
        "normalization steps"
      ],
      "topics": [
        "step_normalize"
      ]
    },
    {
      "page": "step_novel",
      "title": "Simple value assignments for novel factor levels",
      "concept": [
        "dummy variable and encoding steps"
      ],
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