Helpers for parsing out the R functions
  and packages used in R scripts and notebooks.

Implementation of two sample comparison procedures based on
    median-based statistical tests for functional data, introduced in
    Smida et al (2022) <doi:10.1080/10485252.2022.2064997>.  Other
    competitive state-of-the-art approaches proposed by Chakraborty and
    Chaudhuri (2015) <doi:10.1093/biomet/asu072>, Horvath et al (2013)
    <doi:10.1111/j.1467-9868.2012.01032.x> or Cuevas et al (2004)
    <doi:10.1016/j.csda.2003.10.021> are also included in the package, as
    well as procedures to run test result comparisons and power analysis
    using simulations.

A collection of methods for modeling time-to-event data using both functional and scalar predictors. It implements functional data analysis techniques for estimation and inference, allowing researchers to assess the impact of functional covariates on survival outcomes, including time-to-single event and recurrent event outcomes.

Contains functions to compute the functional tangential angle pseudo-depth and its robustified version from the paper by Kuhnt and Rehage (2016). See Kuhnt, S.; Rehage, A. (2016): An angle-based multivariate functional pseudo-depth for shape outlier detection, JMVA 146, 325-340, <doi:10.1016/j.jmva.2015.10.016> for details.

Nonparametric estimators and tests for time series analysis. The functions use bootstrap techniques and robust nonparametric difference-based estimators to test for the presence of possibly non-monotonic trends and for synchronicity of trends in multiple time series.

Provides tools to analyze R source code and detect function definitions
    and their internal dependencies across multiple files. Creates interactive network
    visualizations using 'visNetwork' to display function call relationships, with
    detailed tooltips showing function arguments, return values, and documentation.
    Supports both individual files and directory-based analysis with automatic file
    detection. Useful for understanding code structure, identifying dependencies,
    and documenting R projects.