Package: daltoolbox 1.3.747

Eduardo Ogasawara

daltoolbox: Leveraging Experiment Lines to Data Analytics

The natural increase in the complexity of current research experiments and data demands better tools to enhance productivity in Data Analytics. The package is a framework designed to address the modern challenges in data analytics workflows. The package is inspired by Experiment Line concepts. It aims to provide seamless support for users in developing their data mining workflows by offering a uniform data model and method API. It enables the integration of various data mining activities, including data preprocessing, classification, regression, clustering, and time series prediction. It also offers options for hyper-parameter tuning and supports integration with existing libraries and languages. Overall, the package provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.

Authors:Eduardo Ogasawara [aut, ths, cre], Ana Carolina Sá [aut], Antonio Castro [aut], Caio Santos [aut], Diego Carvalho [ctb], Diego Salles [aut], Eduardo Bezerra [ctb], Esther Pacitti [ctb], Fabio Porto [ctb], Janio Lima [aut], Lucas Tavares [aut], Rafaelli Coutinho [ctb], Rebecca Salles [aut], Vinicius Saidy [aut], CEFET/RJ [cph]

daltoolbox_1.3.747.tar.gz
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manual.pdf |manual.html✨
card.svg |card.png
daltoolbox/json (API)

# Install 'daltoolbox' in R:
install.packages('daltoolbox', repos = c('https://cefet-rj-dal.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cefet-rj-dal/daltoolbox/issues

Datasets:

On CRAN:

Conda:

8.27 score 6 stars 4 packages 1.8k scripts 503 downloads 118 exports 82 dependencies

Last updated from:9885264cfa. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK234
source / vignettesOK258
linux-release-x86_64OK231
macos-release-arm64OK139
macos-oldrel-arm64OK141
windows-develOK186
windows-releaseOK165
windows-oldrelOK196
wasm-releaseOK141

Exports:actionadjust_class_labeladjust_data.frameadjust_factoradjust_matrixaggregationautoenc_base_eautoenc_base_edbal_oversamplingbal_subsamplingcateg_mappingcla_baggingcla_boostingcla_dtreecla_glmcla_glmnetcla_knncla_majoritycla_mlpcla_multinomcla_nbcla_rfcla_rpartcla_svmcla_tunecla_xgboostclassificationclu_tuneclustercluster_cmeanscluster_dbscancluster_gmmcluster_hclustcluster_kmeanscluster_louvain_graphcluster_pamclusterercluutilsdal_basedal_learnerdal_transformdal_tunedata_samplediscoverdt_pcaevaluatefeature_generationfeature_selection_corrfeature_selection_fssfeature_selection_info_gainfeature_selection_lassofeature_selection_relieffeature_selection_stepwisefitfit_curvature_maxfit_curvature_minhierarchy_cutimputation_predictiveimputation_simpleimputation_treeinverse_transformk_foldminmaxna_removaloutliers_boxplotoutliers_gaussianpat_aprioripat_cspadepat_eclatpattern_minerpatutilsplot_barplot_boxplotplot_boxplot_classplot_correlationplot_dendrogramplot_densityplot_density_classplot_groupedbarplot_histplot_lollipopplot_pairplot_pair_advplot_parallelplot_pieplotplot_pixelplot_pointsplot_radarplot_scatterplot_seriesplot_stackedbarplot_tsplot_ts_predpredictorreg_dtreereg_knnreg_lmreg_mlpreg_rfreg_svmreg_tuneregressionsample_balancesample_groupssample_randomsample_simplesample_stratifiedselect_hyperset_paramssmoothingsmoothing_clustersmoothing_freqsmoothing_intersmoothing_quantizationtrain_testtrain_test_from_foldstransformzscore

Dependencies:arulesarulesSequencescaretclasscliclockclustercodetoolscpp11data.tabledbscandiagramdigestdplyre1071farverFNNforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmclustModelMetricsnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6randomForestRColorBrewerRcpprecipesreshapereshape2rlangrpartS7scalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetreetzdbutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Actionaction
Action implementation for transformaction.dal_transform
Adjust categorical mappingadjust_class_label
Adjust to data frameadjust_data.frame
Adjust factorsadjust_factor
Adjust to matrixadjust_matrix
Aggregation by groupsaggregation
Autoencoder base (encoder)autoenc_base_e
Autoencoder base (encoder + decoder)autoenc_base_ed
Random or SMOTE-based class oversamplingbal_oversampling
Random class undersamplingbal_subsampling
Boston Housing Data (Regression)Boston
Categorical mapping (one‑hot encoding)categ_mapping
Bagging (ipred)cla_bagging
Boosting (adabag)cla_boosting
Decision Tree for classificationcla_dtree
Logistic regression (GLM)cla_glm
LASSO logistic regression (glmnet)cla_glmnet
K-Nearest Neighbors (KNN) Classificationcla_knn
Majority baseline classifiercla_majority
MLP for classificationcla_mlp
Multinomial logistic regressioncla_multinom
Naive Bayes Classifiercla_nb
Random Forest for classificationcla_rf
CART (rpart)cla_rpart
SVM for classificationcla_svm
Classification tuning (k-fold CV)cla_tune
XGBoostcla_xgboost
Classification base classclassification
Clustering tuning (intrinsic metric)clu_tune
Clustercluster
Fuzzy c-meanscluster_cmeans
DBSCANcluster_dbscan
Gaussian mixture model clustering (GMM)cluster_gmm
Hierarchical clusteringcluster_hclust
k-meanscluster_kmeans
Louvain community detectioncluster_louvain_graph
PAM (Partitioning Around Medoids)cluster_pam
Clustererclusterer
Clustering utilitiescluutils
Class dal_basedal_base
Graphics utilitiesdal_graphics
DAL Learner (base class)dal_learner
DAL Transformdal_transform
DAL Tune (base for hyperparameter search)dal_tune
Data sampling abstractionsdata_sample
Discoverdiscover
PCAdt_pca
Evaluateevaluate
Feature generationfeature_generation
Feature selection by correlationfeature_selection_corr
Feature selection by forward stepwise searchfeature_selection_fss
Feature selection by information gainfeature_selection_info_gain
Feature selection by lassofeature_selection_lasso
Feature selection by RELIEFfeature_selection_relief
Feature selection by stepwise model selectionfeature_selection_stepwise
Fitfit
Maximum curvature analysis (elbow detection)fit_curvature_max
Minimum curvature analysis (elbow detection)fit_curvature_min
tune hyperparameters of ml modelfit.cla_tune
fit dbscan modelfit.cluster_dbscan
Hierarchy mapping by cuthierarchy_cut
Predictive imputation baseimputation_predictive
Simple imputationimputation_simple
Tree-based predictive imputationimputation_tree
Inverse Transforminverse_transform
K-fold samplingk_fold
Min-max normalizationminmax
Missing value removalna_removal
Outlier removal by boxplot (IQR rule)outliers_boxplot
Outlier removal by Gaussian 3-sigma ruleoutliers_gaussian
Apriori rulespat_apriori
cSPADE sequencespat_cspade
ECLAT itemsetspat_eclat
Pattern minerpattern_miner
Pattern mining utilitiespatutils
Plot bar graphplot_bar
Plot boxplotplot_boxplot
Boxplot per classplot_boxplot_class
Plot correlationplot_correlation
Plot dendrogramplot_dendrogram
Plot densityplot_density
Plot density per classplot_density_class
Plot grouped barplot_groupedbar
Plot histogramplot_hist
Plot lollipopplot_lollipop
Plot scatter matrixplot_pair
Plot advanced scatter matrixplot_pair_adv
Plot parallel coordinatesplot_parallel
Plot pieplot_pieplot
Plot pixel visualizationplot_pixel
Plot pointsplot_points
Plot radarplot_radar
Scatter graphplot_scatter
Plot seriesplot_series
Plot stacked barplot_stackedbar
Plot time series chartplot_ts
Plot time series with predictionsplot_ts_pred
Predictor (base for classification/regression)predictor
Decision Tree for regressionreg_dtree
K-Nearest Neighbors (KNN) Regressionreg_knn
Linear regression (lm)reg_lm
MLP for regressionreg_mlp
Random Forest for regressionreg_rf
SVM for regressionreg_svm
Regression tuning (k-fold CV)reg_tune
Regression base classregression
Class balancing (up/down sampling)sample_balance
Group samplingsample_groups
Random samplingsample_random
Simple samplingsample_simple
Stratified samplingsample_stratified
Selection of hyperparametersselect_hyper
selection of hyperparametersselect_hyper.cla_tune
Assign parametersset_params
Default Assign parametersset_params.default
Smoothing (binning/quantization)smoothing
Smoothing by class-aware clusteringsmoothing_cluster
Smoothing by equal frequencysmoothing_freq
Smoothing by equal intervalsmoothing_inter
Smoothing by quantization (k-means)smoothing_quantization
Train-Test Partitiontrain_test
k-fold training and test partition objecttrain_test_from_folds
Transformtransform
Z-score normalizationzscore