{
  "_id": "6a1ee67fb401979e73411572",
  "Package": "daltoolbox",
  "Title": "Leveraging Experiment Lines to Data Analytics",
  "Version": "1.3.747",
  "Authors@R": "c(    person(given = \"Eduardo\", family = \"Ogasawara\", role = c(\"aut\", \"ths\", \"cre\"),\nemail = \"eogasawara@ieee.org\", comment = c(ORCID = \"0000-0002-0466-0626\")),\nperson(given = \"Ana Carolina\", family = \"Sá\", role = c(\"aut\"), email = \"ana.silva.17@aluno.cefet-rj.br\"),\nperson(given = \"Antonio\", family = \"Castro\", role = c(\"aut\"), email = \"antonio.castro@eic.cefet-rj.br\"),\nperson(given = \"Caio\", family = \"Santos\", role = c(\"aut\"), email = \"caio.souza.4@aluno.cefet-rj.br\"),\nperson(given = \"Diego\", family = \"Carvalho\", role = c(\"ctb\"), email = \"d.carvalho@ieee.org\"),\nperson(given = \"Diego\", family = \"Salles\", role = c(\"aut\"), email = \"diego.salles@eic.cefet-rj.br\"),\nperson(given = \"Eduardo\", family = \"Bezerra\", role = c(\"ctb\"), email = \"ebezerra@cefet-rj.br\"),\nperson(given = \"Esther\", family = \"Pacitti\", role = c(\"ctb\"), email = \"esther.pacitti@lirmm.fr\"),\nperson(given = \"Fabio\", family = \"Porto\", role = c(\"ctb\"), email = \"fporto@lncc.br\"),\nperson(given = \"Janio\", family = \"Lima\", role = c(\"aut\"), email = \"janio.lima@eic.cefet-rj.br\"),\nperson(given = \"Lucas\", family = \"Tavares\", role = c(\"aut\"), email = \"lucas.tavares@eic.cefet-rj.br\"),\nperson(given = \"Rafaelli\", family = \"Coutinho\", role = c(\"ctb\"), email = \"rafaelli.coutinho@cefet-rj.br\"),\nperson(given = \"Rebecca\", family = \"Salles\", role = \"aut\", email = \"rebecca.salles@eic.cefet-rj.br\"),\nperson(given = \"Vinicius\", family = \"Saidy\", role = \"aut\", email = \"vinicius.saidy@aluno.cefet-rj.br\"),\nperson(given = \"CEFET/RJ\", role = \"cph\")\n)",
  "Description": "The natural increase in the complexity of current research\nexperiments and data demands better tools to enhance\nproductivity in Data Analytics. The package is a framework\ndesigned to address the modern challenges in data analytics\nworkflows. The package is inspired by Experiment Line concepts.\nIt aims to provide seamless support for users in developing\ntheir data mining workflows by offering a uniform data model\nand method API. It enables the integration of various data\nmining activities, including data preprocessing,\nclassification, regression, clustering, and time series\nprediction. It also offers options for hyper-parameter tuning\nand supports integration with existing libraries and languages.\nOverall, the package provides researchers with a comprehensive\nset of functionalities for data science, promoting ease of use,\nextensibility, and integration with various tools and\nlibraries. Information on Experiment Line is based on Ogasawara\net al. (2009) <doi:10.1007/978-3-642-02279-1_20>.",
  "License": "MIT + file LICENSE",
  "URL": "https://cefet-rj-dal.github.io/daltoolbox/,\nhttps://github.com/cefet-rj-dal/daltoolbox",
  "BugReports": "https://github.com/cefet-rj-dal/daltoolbox/issues",
  "Encoding": "UTF-8",
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  "Repository": "https://cefet-rj-dal.r-universe.dev",
  "Date/Publication": "2026-05-20 02:33:43 UTC",
  "RemoteUrl": "https://github.com/cefet-rj-dal/daltoolbox",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-20 03:02:34 UTC",
    "User": "root"
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  "Author": "Eduardo Ogasawara [aut, ths, cre] (ORCID:\n<https://orcid.org/0000-0002-0466-0626>),\nAna Carolina Sá [aut],\nAntonio Castro [aut],\nCaio Santos [aut],\nDiego Carvalho [ctb],\nDiego Salles [aut],\nEduardo Bezerra [ctb],\nEsther Pacitti [ctb],\nFabio Porto [ctb],\nJanio Lima [aut],\nLucas Tavares [aut],\nRafaelli Coutinho [ctb],\nRebecca Salles [aut],\nVinicius Saidy [aut],\nCEFET/RJ [cph]",
  "Maintainer": "Eduardo Ogasawara <eogasawara@ieee.org>",
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  "_created": "2026-05-20T03:02:34.000Z",
  "_published": "2026-06-02T14:19:43.467Z",
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    "adjust_class_label",
    "adjust_data.frame",
    "adjust_factor",
    "adjust_matrix",
    "aggregation",
    "autoenc_base_e",
    "autoenc_base_ed",
    "bal_oversampling",
    "bal_subsampling",
    "categ_mapping",
    "cla_bagging",
    "cla_boosting",
    "cla_dtree",
    "cla_glm",
    "cla_glmnet",
    "cla_knn",
    "cla_majority",
    "cla_mlp",
    "cla_multinom",
    "cla_nb",
    "cla_rf",
    "cla_rpart",
    "cla_svm",
    "cla_tune",
    "cla_xgboost",
    "classification",
    "clu_tune",
    "cluster",
    "cluster_cmeans",
    "cluster_dbscan",
    "cluster_gmm",
    "cluster_hclust",
    "cluster_kmeans",
    "cluster_louvain_graph",
    "cluster_pam",
    "clusterer",
    "cluutils",
    "dal_base",
    "dal_learner",
    "dal_transform",
    "dal_tune",
    "data_sample",
    "discover",
    "dt_pca",
    "evaluate",
    "feature_generation",
    "feature_selection_corr",
    "feature_selection_fss",
    "feature_selection_info_gain",
    "feature_selection_lasso",
    "feature_selection_relief",
    "feature_selection_stepwise",
    "fit",
    "fit_curvature_max",
    "fit_curvature_min",
    "hierarchy_cut",
    "imputation_predictive",
    "imputation_simple",
    "imputation_tree",
    "inverse_transform",
    "k_fold",
    "minmax",
    "na_removal",
    "outliers_boxplot",
    "outliers_gaussian",
    "pat_apriori",
    "pat_cspade",
    "pat_eclat",
    "pattern_miner",
    "patutils",
    "plot_bar",
    "plot_boxplot",
    "plot_boxplot_class",
    "plot_correlation",
    "plot_dendrogram",
    "plot_density",
    "plot_density_class",
    "plot_groupedbar",
    "plot_hist",
    "plot_lollipop",
    "plot_pair",
    "plot_pair_adv",
    "plot_parallel",
    "plot_pieplot",
    "plot_pixel",
    "plot_points",
    "plot_radar",
    "plot_scatter",
    "plot_series",
    "plot_stackedbar",
    "plot_ts",
    "plot_ts_pred",
    "predictor",
    "reg_dtree",
    "reg_knn",
    "reg_lm",
    "reg_mlp",
    "reg_rf",
    "reg_svm",
    "reg_tune",
    "regression",
    "sample_balance",
    "sample_groups",
    "sample_random",
    "sample_simple",
    "sample_stratified",
    "select_hyper",
    "set_params",
    "smoothing",
    "smoothing_cluster",
    "smoothing_freq",
    "smoothing_inter",
    "smoothing_quantization",
    "train_test",
    "train_test_from_folds",
    "transform",
    "zscore"
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      "title": "Boston Housing Data (Regression)",
      "object": "Boston",
      "file": "Boston.RData",
      "class": [
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        "zn",
        "indus",
        "chas",
        "nox",
        "rm",
        "age",
        "dis",
        "rad",
        "tax",
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        "black",
        "lstat",
        "medv"
      ],
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      "table": true,
      "tojson": true
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      "title": "Action",
      "topics": [
        "action"
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      "title": "Action implementation for transform",
      "topics": [
        "action.dal_transform"
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    },
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      "title": "Adjust categorical mapping",
      "topics": [
        "adjust_class_label"
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    },
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      "title": "Adjust to data frame",
      "topics": [
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    {
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      "title": "Adjust factors",
      "topics": [
        "adjust_factor"
      ]
    },
    {
      "page": "adjust_matrix",
      "title": "Adjust to matrix",
      "topics": [
        "adjust_matrix"
      ]
    },
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      "page": "aggregation",
      "title": "Aggregation by groups",
      "topics": [
        "aggregation"
      ]
    },
    {
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      "title": "Autoencoder base (encoder)",
      "topics": [
        "autoenc_base_e"
      ]
    },
    {
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      "title": "Autoencoder base (encoder + decoder)",
      "topics": [
        "autoenc_base_ed"
      ]
    },
    {
      "page": "bal_oversampling",
      "title": "Random or SMOTE-based class oversampling",
      "topics": [
        "bal_oversampling"
      ]
    },
    {
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      "title": "Random class undersampling",
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        "bal_subsampling"
      ]
    },
    {
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      "title": "Boston Housing Data (Regression)",
      "topics": [
        "Boston"
      ]
    },
    {
      "page": "categ_mapping",
      "title": "Categorical mapping (one‑hot encoding)",
      "topics": [
        "categ_mapping"
      ]
    },
    {
      "page": "cla_bagging",
      "title": "Bagging (ipred)",
      "topics": [
        "cla_bagging"
      ]
    },
    {
      "page": "cla_boosting",
      "title": "Boosting (adabag)",
      "topics": [
        "cla_boosting"
      ]
    },
    {
      "page": "cla_dtree",
      "title": "Decision Tree for classification",
      "topics": [
        "cla_dtree"
      ]
    },
    {
      "page": "cla_glm",
      "title": "Logistic regression (GLM)",
      "topics": [
        "cla_glm"
      ]
    },
    {
      "page": "cla_glmnet",
      "title": "LASSO logistic regression (glmnet)",
      "topics": [
        "cla_glmnet"
      ]
    },
    {
      "page": "cla_knn",
      "title": "K-Nearest Neighbors (KNN) Classification",
      "topics": [
        "cla_knn"
      ]
    },
    {
      "page": "cla_majority",
      "title": "Majority baseline classifier",
      "topics": [
        "cla_majority"
      ]
    },
    {
      "page": "cla_mlp",
      "title": "MLP for classification",
      "topics": [
        "cla_mlp"
      ]
    },
    {
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      "title": "Multinomial logistic regression",
      "topics": [
        "cla_multinom"
      ]
    },
    {
      "page": "cla_nb",
      "title": "Naive Bayes Classifier",
      "topics": [
        "cla_nb"
      ]
    },
    {
      "page": "cla_rf",
      "title": "Random Forest for classification",
      "topics": [
        "cla_rf"
      ]
    },
    {
      "page": "cla_rpart",
      "title": "CART (rpart)",
      "topics": [
        "cla_rpart"
      ]
    },
    {
      "page": "cla_svm",
      "title": "SVM for classification",
      "topics": [
        "cla_svm"
      ]
    },
    {
      "page": "cla_tune",
      "title": "Classification tuning (k-fold CV)",
      "topics": [
        "cla_tune"
      ]
    },
    {
      "page": "cla_xgboost",
      "title": "XGBoost",
      "topics": [
        "cla_xgboost"
      ]
    },
    {
      "page": "classification",
      "title": "Classification base class",
      "topics": [
        "classification"
      ]
    },
    {
      "page": "clu_tune",
      "title": "Clustering tuning (intrinsic metric)",
      "topics": [
        "clu_tune"
      ]
    },
    {
      "page": "cluster",
      "title": "Cluster",
      "topics": [
        "cluster"
      ]
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    {
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      "title": "Fuzzy c-means",
      "topics": [
        "cluster_cmeans"
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    },
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      "topics": [
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