A toolbox for developing applications, games, simulations, or
    agent-based models in the R terminal. Included functions allow users to move the 
    cursor around the terminal screen, change text colors and attributes, clear the
    screen, hide and show the cursor, map key presses to functions, draw shapes and curves,
    among others. Most functionalities require users to be in a terminal (not the R GUI).

Finds single- and two-arm designs using stochastic curtailment, as described by Law et al. (2022) <doi:10.1080/10543406.2021.2009498> and Law et al. (2021) <doi:10.1002/pst.2067> respectively. Designs can be single-stage or multi-stage. Non-stochastic curtailment is possible as a special case. Desired error-rates, maximum sample size and lower and upper anticipated response rates are inputted and suitable designs are returned with operating characteristics. Stopping boundaries and visualisations are also available. The package can find designs using other approaches, for example designs by Simon (1989) <doi:10.1016/0197-2456(89)90015-9> and Mander and Thompson (2010) <doi:10.1016/j.cct.2010.07.008>. Other features: compare and visualise designs using a weighted sum of expected sample sizes under the null and alternative hypotheses and maximum sample size; visualise any binary outcome design.

Data recorded as paths or trajectories may be suitably described 
  by curves, which are independent of their parametrization. For the space of 
  such curves, the package provides functionalities for reading curves, sampling 
  points on curves, calculating distance between curves and for computing Tukey
  curve depth of a curve w.r.t. to a bundle of curves. For details see 
  Lafaye De Micheaux, Mozharovskyi, and Vimond (2021) <doi:10.48550/arXiv.1901.00180>.

Create model-agnostic response-curve diagnostics for fitted
    prediction models. Supports profile curves, partial dependence,
    individual conditional expectation, and accumulated local effects;
    univariate curves, bivariate surfaces, ensemble summaries across
    multiple models, ALE-based interaction ranking, and optional
    raster-linked exploration with 'terra' and 'shiny'. Static displays
    are returned as 'ggplot2' plots. For more details on the methods see
    Molnar (2025) <https://christophm.github.io/interpretable-ml-book/>.