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DAL Toolbox

The goal of DAL Toolbox is to provide a series data analytics functions organized as a framework. It supports data preprocessing, classification, regression, clustering, and time series prediction 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")

Examples

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

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

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

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

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

Time series: https://github.com/cefet-rj-dal/daltoolbox/tree/main/timeseries/

The examples are organized according to general (data preprocessing), clustering, classification, regression, and time series functions. This version has Python integration with Pytorch.

library(daltoolbox)
#> 
#> Attaching package: 'daltoolbox'
#> The following object is masked from 'package:base':
#> 
#>     transform
## loading DAL Toolbox

Bugs and new features request

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

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Version

Install

install.packages('daltoolbox')

Monthly Downloads

966

Version

1.2.727

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Eduardo Ogasawara

Last Published

June 28th, 2025

Functions in daltoolbox (1.2.727)

cla_majority

Majority Classification
cla_mlp

MLP for classification
clu_tune

Clustering Tune
cla_knn

K Nearest Neighbor Classification
cla_nb

Naive Bayes Classifier
cluster

Cluster
outliers_boxplot

outliers_boxplot
fit.cluster_dbscan

fit dbscan model
plot_boxplot

Plot boxplot
clusterer

Clusterer
cla_rf

Random Forest for classification
fit_curvature_max

maximum curvature analysis
plot_bar

Plot bar graph
dal_base

Class dal_base
plot_boxplot_class

Boxplot per class
cluster_kmeans

k-means
evaluate

Evaluate
dal_transform

DAL Transform
dt_pca

PCA
cluster_pam

PAM
dal_learner

DAL Learner
fit

Fit
outliers_gaussian

outliers_gaussian
cluster_dbscan

DBSCAN
plot_radar

Plot radar
select_hyper

Selection hyper parameters
plot_density

Plot density
predictor

DAL Predict
reg_dtree

Decision Tree for regression
train_test_from_folds

k-fold training and test partition object
train_test

Train-Test Partition
select_hyper.cla_tune

selection of hyperparameters
dal_tune

DAL Tune
plot_scatter

Scatter graph
data_sample

Data Sample
sample_random

Sample Random
sample_stratified

Stratified Random Sampling
smoothing_freq

Smoothing by Freq
smoothing_inter

Smoothing by interval
fit_curvature_min

minimum curvature analysis
inverse_transform

Inverse Transform
k_fold

K-fold sampling
minmax

Min-max normalization
plot_groupedbar

Plot grouped bar
plot_density_class

Plot density per class
plot_series

Plot series
plot_stackedbar

Plot stacked bar
plot_ts

Plot time series chart
plot_ts_pred

Plot a time series chart with predictions
reg_rf

Random Forest for regression
fit.cla_tune

tune hyperparameters of ml model
reg_tune

Regression Tune
regression

Regression
reg_svm

SVM for regression
plot_lollipop

Plot lollipop
plot_hist

Plot histogram
plot_pieplot

Plot pie
plot_points

Plot points
set_params

Assign parameters
reg_knn

knn regression
set_params.default

Default Assign parameters
reg_mlp

MLP for regression
zscore

Z-score normalization
transform

Transform
smoothing

Smoothing
smoothing_cluster

Smoothing by cluster
adjust_data.frame

Adjust to data frame
categ_mapping

Categorical mapping
autoenc_base_ed

Autoencoder - Encode-decode
action.dal_transform

Action implementation for transform
adjust_matrix

Adjust to matrix
autoenc_base_e

Autoencoder - Encode
adjust_class_label

Adjust categorical mapping
Boston

Boston Housing Data (Regression)
action

Action
cla_svm

SVM for classification
cla_dtree

Decision Tree for classification
cla_tune

Classification Tune
adjust_factor

Adjust factors
classification

classification