Learn R Programming

⚠️There's a newer version (1.2.727) of this package.Take me there.

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)
#> Registered S3 method overwritten by 'quantmod':
#>   method            from
#>   as.zoo.data.frame zoo
#> Registered S3 methods overwritten by 'forecast':
#>   method  from 
#>   head.ts stats
#>   tail.ts stats
#> 
#> 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

Copy Link

Version

Install

install.packages('daltoolbox')

Monthly Downloads

966

Version

1.2.717

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Eduardo Ogasawara

Last Published

June 21st, 2025

Functions in daltoolbox (1.2.717)

fit

Fit
dal_base

Class dal_base
cluster_pam

PAM
clusterer

Clusterer
plot_groupedbar

Plot grouped bar
autoenc_base_e

Autoencoder - Encode
autoenc_base_ed

Autoencoder - Encode-decode
plot_hist

Plot histogram
dal_transform

DAL Transform
reg_rf

Random Forest for regression
plot_boxplot

Plot boxplot
reg_mlp

MLP for regression
plot_boxplot_class

Boxplot per class
dal_tune

DAL Tune
sample_random

Sample Random
sample_stratified

Stratified Random Sampling
plot_points

Plot points
transform

Transform
ts_arima

ARIMA
outliers_gaussian

outliers_gaussian
plot_bar

Plot bar graph
cla_knn

K Nearest Neighbor Classification
plot_radar

Plot radar
cla_majority

Majority Classification
cla_tune

Classification Tune
fit.cluster_dbscan

fit dbscan model
classification

classification
fit.cla_tune

tune hyperparameters of ml model
ts_regsw

TSRegSW
predictor

DAL Predict
plot_ts_pred

Plot a time series chart with predictions
set_params

Assign parameters
set_params.default

Default Assign parameters
dal_learner

DAL Learner
minmax

Min-max normalization
do_predict

Predict Time Series Model
dt_pca

PCA
ts_sample

Time Series Sample
cluster

Cluster
outliers_boxplot

outliers_boxplot
plot_scatter

Scatter graph
clu_tune

Clustering Tune
data_sample

Data Sample
plot_series

Plot series
reg_dtree

Decision Tree for regression
inverse_transform

Inverse Transform
k_fold

K-fold sampling
[.ts_data

Subset Extraction for Time Series Data
smoothing_inter

Smoothing by interval
reg_knn

knn regression
regression

Regression
sMAPE.ts

sMAPE
smoothing_cluster

Smoothing by cluster
smoothing_freq

Smoothing by Freq
ts_projection

Time Series Projection
ts_reg

TSReg
do_fit

Fit Time Series Model
fit_curvature_min

minimum curvature analysis
fit_curvature_max

maximum curvature analysis
plot_stackedbar

Plot stacked bar
plot_density

Plot density
plot_density_class

Plot density per class
plot_ts

Plot time series chart
select_hyper

Selection hyper parameters
plot_lollipop

Plot lollipop
select_hyper.cla_tune

selection of hyperparameters
train_test

Train-Test Partition
train_test_from_folds

k-fold training and test partition object
zscore

Z-score normalization
reg_svm

SVM for regression
plot_pieplot

Plot pie
reg_tune

Regression Tune
ts_head

Extract the First Observations from a ts_data Object
ts_data

ts_data
sin_data

Time series example dataset
smoothing

Smoothing
action.dal_transform

Action implementation for transform
adjust_class_label

Adjust categorical mapping
adjust_matrix

Adjust to matrix
R2.ts

R2
action

Action
adjust_ts_data

Adjust ts_data
categ_mapping

Categorical mapping
cla_dtree

Decision Tree for classification
adjust_data.frame

Adjust to data frame
adjust_factor

Adjust factors
Boston

Boston Housing Data (Regression)
MSE.ts

MSE
cla_rf

Random Forest for classification
cla_mlp

MLP for classification
cla_svm

SVM for classification
cla_nb

Naive Bayes Classifier
cluster_dbscan

DBSCAN
cluster_kmeans

k-means
evaluate

Evaluate