Provides a framework for creating high-performance 'R'
    packages powered by the 'Rust' programming language using the
    'extendr' Rust crate.  It offers 'usethis'-like functions to scaffold
    and develop 'Rust' powered 'R' packages, including utilities for
    publishing to CRAN, managing dependencies, configuring development
    environments, and rendering 'Rust' code in 'knitr' documents.
    Additionally, it provides functions to compile and evaluate 'Rust'
    code directly from 'R' for interactive development.

Facilitate data preparation for data collected on 'WEXTOR' <https://wextor.eu>, created by Reips and Neuhaus (2002) <doi:10.3758/bf03195449>. Perform plausibility and other checks and make use of cool color palettes and themes for data visualization.

Provides an interface to the Facebook API.

Load data by campaigns, ads, ad sets and insights, ad account and business manager 
    from Facebook Marketing API into R. For more details see official documents by Facebook 
    Marketing API <https://developers.facebook.com/docs/marketing-api>.

The 'rfacts' package is an R interface to the
  Fixed and Adaptive Clinical Trial Simulator ('FACTS')
  on Unix-like systems. It programmatically invokes 'FACTS' to run clinical
  trial simulations, and it aggregates simulation output data
  into tidy data frames. These capabilities provide end-to-end
  automation for large-scale simulation pipelines, and
  they enhance computational reproducibility.
  For more information on 'FACTS' itself,
  please visit <https://www.berryconsultants.com/software/>.

Autoencoding Random Forests ('RFAE') provide a method to
    autoencode mixed-type tabular data using Random Forests ('RF'), which 
    involves projecting the data to a latent feature space of user-chosen 
    dimensionality (usually a lower dimension), and then decoding the latent 
    representations back into the input space. The encoding stage is useful for 
    feature engineering and data visualisation tasks, akin to how principal
    component analysis ('PCA') is used, and the decoding stage is useful
    for compression and denoising tasks. At its core, 'RFAE' is a
    post-processing pipeline on a trained random forest model. This means
    that it can accept any trained RF of 'ranger' object type: 'RF', 'URF' or
    'ARF'. Because of this, it inherits Random Forests' robust performance and 
    capacity to seamlessly handle mixed-type tabular data. For more details, see 
    Vu et al. (2025) <doi:10.48550/arXiv.2505.21441>.