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.
Graphics: https://github.com/cefet-rj-dal/daltoolbox/tree/main/examples/graphics/
Transformation: https://github.com/cefet-rj-dal/daltoolbox/tree/main/examples/transf/
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), clustering, classification, regression, and time series functions.
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")