Learn R Programming

DAL Toolbox

As research experiments grow in scale and complexity, data analytics demands tools that go beyond isolated functions. DAL Toolbox is a framework designed to meet these modern challenges by organizing a comprehensive set of data analytics capabilities into an integrated workflow environment. Inspired by the Experiment Line model doi:10.1007/978-3-642-02279-1_20, it supports essential tasks such as data preprocessing, classification, regression, clustering, and time series prediction. With a unified data model, consistent method API, and support for hyperparameter tuning, DAL Toolbox enables the seamless construction and execution of end-to-end analytics pipelines. It also offers easy integration with existing libraries and languages, promoting usability, extensibility, and reproducibility in data science.


Examples

Graphics: https://github.com/cefet-rj-dal/daltoolbox/tree/main/examples/graphics/

Transformation: https://github.com/cefet-rj-dal/daltoolbox/tree/main/examples/transf/

Custom: https://github.com/cefet-rj-dal/daltoolbox/tree/main/Rmd/custom/

Classification: https://github.com/cefet-rj-dal/daltoolbox/tree/main/examples/classification/

Clustering: https://github.com/cefet-rj-dal/daltoolbox/tree/main/examples/clustering/

Regression: https://github.com/cefet-rj-dal/daltoolbox/tree/main/examples/regression/

The examples are organized according to general (data preprocessing), custom extensions, clustering, classification, regression, and time series functions.


Installation

The latest version of DAL Toolbox at CRAN is available at: https://CRAN.R-project.org/package=daltoolbox

You can install the stable version of DAL Toolbox from CRAN with:

install.packages("daltoolbox")

You can install the development version of DAL Toolbox from GitHub https://github.com/cefet-rj-dal/daltoolbox with:

library(devtools)
devtools::install_github("cefet-rj-dal/daltoolbox", force=TRUE, dependencies=FALSE, upgrade="never")

Bugs and new features request

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

Copy Link

Version

Install

install.packages('daltoolbox')

Monthly Downloads

644

Version

1.3.727

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Eduardo Ogasawara

Last Published

March 12th, 2026

Functions in daltoolbox (1.3.727)

cla_rpart

CART (rpart)
cla_dtree

Decision Tree for classification
cla_boosting

Boosting (adabag)
cluster_louvain_graph

Louvain community detection
cla_mlp

MLP for classification
data_sample

Data sampling abstractions
discover

Discover
cla_svm

SVM for classification
cluster_kmeans

k-means
evaluate

Evaluate
classification

Classification base class
clu_tune

Clustering tuning (intrinsic metric)
cluster_hclust

Hierarchical clustering
dt_pca

PCA
cla_tune

Classification tuning (k-fold CV)
cla_xgboost

XGBoost
clusterer

Clusterer
dal_base

Class dal_base
feature_generation

Feature generation
feature_selection_corr

Feature selection by correlation
bal_oversampling

Random or SMOTE-based class oversampling
feature_selection_relief

Feature selection by RELIEF
feature_selection_lasso

Feature selection by lasso
plot_density_class

Plot density per class
plot_groupedbar

Plot grouped bar
cluster_gmm

Gaussian mixture model clustering (GMM)
fit_curvature_max

Maximum curvature analysis (elbow detection)
cluster_dbscan

DBSCAN
cla_multinom

Multinomial logistic regression
plot_pieplot

Plot pie
plot_parallel

Plot parallel coordinates
dal_learner

DAL Learner (base class)
fit.cla_tune

tune hyperparameters of ml model
fit.cluster_dbscan

fit dbscan model
cluster_pam

PAM (Partitioning Around Medoids)
inverse_transform

Inverse Transform
dal_graphics

Graphics utilities
k_fold

K-fold sampling
plot_dendrogram

Plot dendrogram
plot_hist

Plot histogram
plot_density

Plot density
plot_lollipop

Plot lollipop
dal_transform

DAL Transform
plot_ts

Plot time series chart
imputation_simple

Simple imputation
hierarchy_cut

Hierarchy mapping by cut
dal_tune

DAL Tune (base for hyperparameter search)
bal_subsampling

Random class undersampling
pat_apriori

Apriori rules
fit_curvature_min

Minimum curvature analysis (elbow detection)
cla_nb

Naive Bayes Classifier
feature_selection_stepwise

Feature selection by stepwise model selection
plot_series

Plot series
cla_rf

Random Forest for classification
cluster

Cluster
cluster_cmeans

Fuzzy c-means
fit

Fit
train_test

Train-Test Partition
plot_ts_pred

Plot time series with predictions
outliers_gaussian

Outlier removal by Gaussian 3-sigma rule
outliers_boxplot

Outlier removal by boxplot (IQR rule)
plot_stackedbar

Plot stacked bar
smoothing_freq

Smoothing by equal frequency
smoothing_inter

Smoothing by equal interval
sample_stratified

Stratified sampling
sample_simple

Simple sampling
plot_bar

Plot bar graph
train_test_from_folds

k-fold training and test partition object
predictor

Predictor (base for classification/regression)
regression

Regression base class
reg_dtree

Decision Tree for regression
sample_balance

Class balancing (up/down sampling)
plot_boxplot

Plot boxplot
plot_points

Plot points
plot_pixel

Plot pixel visualization
minmax

Min-max normalization
feature_selection_fss

Feature selection by forward stepwise search
na_removal

Missing value removal
feature_selection_info_gain

Feature selection by information gain
pat_cspade

cSPADE sequences
pat_eclat

ECLAT itemsets
reg_svm

SVM for regression
pattern_miner

Pattern miner
transform

Transform
reg_mlp

MLP for regression
plot_boxplot_class

Boxplot per class
plot_correlation

Plot correlation
plot_pair_adv

Plot advanced scatter matrix
plot_pair

Plot scatter matrix
reg_rf

Random Forest for regression
sample_random

Random sampling
sample_cluster

Cluster sampling
reg_lm

Linear regression (lm)
select_hyper.cla_tune

selection of hyperparameters
reg_knn

K-Nearest Neighbors (KNN) Regression
plot_radar

Plot radar
select_hyper

Selection of hyperparameters
plot_scatter

Scatter graph
smoothing

Smoothing (binning/quantization)
smoothing_cluster

Smoothing by clustering (k-means)
zscore

Z-score normalization
reg_tune

Regression tuning (k-fold CV)
set_params.default

Default Assign parameters
set_params

Assign parameters
Boston

Boston Housing Data (Regression)
adjust_class_label

Adjust categorical mapping
autoenc_base_e

Autoencoder base (encoder)
adjust_data.frame

Adjust to data frame
adjust_matrix

Adjust to matrix
cla_glm

Logistic regression (GLM)
cla_knn

K-Nearest Neighbors (KNN) Classification
autoenc_base_ed

Autoencoder base (encoder + decoder)
cla_bagging

Bagging (ipred)
categ_mapping

Categorical mapping (one‑hot encoding)
action

Action
cla_majority

Majority baseline classifier
cla_glmnet

LASSO logistic regression (glmnet)
aggregation

Aggregation by groups
action.dal_transform

Action implementation for transform
adjust_factor

Adjust factors