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Python-Based Extensions for Data Analytics Workflows provides Python-based extensions to enhance data analytics workflows, particularly for tasks involving data preprocessing and predictive modeling. It includes tools for:
These capabilities leverage Python libraries via the
reticulate
interface, enabling seamless integration with
the broader Python machine learning ecosystem. The package supports
instance selection and hybrid workflows that combine R and Python
functionalities for flexible and reproducible analytical pipelines.
The architecture is inspired by the Experiment Lines
approach, which promotes modularity, extensibility, and interoperability
across tools.
More information on Experiment Lines is available in Ogasawara et
al. (2009).
You can install the latest stable version from CRAN:
install.packages("daltoolboxdp")
To install the development version from GitHub:
# install.packages("devtools")
library(devtools)
::install_github("cefet-rj-dal/daltoolboxdp", force = TRUE, dependencies = FALSE, upgrade = "never") devtools
Example scripts are available at:
library(daltoolboxdp)
#> 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
# Example usage (replace with actual function when available)
# e.g., data <- my_sampler_function(data, method = "undersample")
Please report issues or suggest new features via:
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.