Analytically calculates the operating characteristics of
    single-stage and two-stage basket trials with equal sample sizes using the
	power prior design by Baumann et al. (2024) <doi:10.48550/arXiv.2309.06988>
	and the design by Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>.

A unified framework for optimizing
    basket trial designs. To this end, the package supplies several utility
    functions and also a function for executing optimization algorithms on basket
    trial designs. The considered utility functions are discussed in
    Sauer et al. (2025) <doi:10.1371/journal.pone.0323097>.

Provides a unified syntax for the simulation-based comparison of 
  different single-stage basket trial designs with a binary endpoint and equal 
  sample sizes in all baskets. Methods include the designs by 
	Baumann et al. (2025) <doi:10.1080/19466315.2024.2402275>, 
	Schmitt and Baumann (2025) <doi:10.1080/19466315.2025.2486231>,
	Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>,
	Berry et al. (2020) <doi:10.1177/1740774513497539>, and
	Neuenschwander et al. (2016) <doi:10.1002/pst.1730>. For the 
	latter two designs, the functions are mostly 
	wrappers for functions provided by the package 'bhmbasket'.

A simple interface to switch between 
    two methods for calculating basket trial characteristics, numerical 
    integration ("exact") and Monte Carlo simulation ("simulated") for the
    basket trial design by 
    Fujikawa et al. 2020 <doi:10.1002/bimj.201800404>. 
    The exact implementation is from the 'baskexact' package, see
    Baumann (2024) <doi:10.1016/j.softx.2024.101793>. The simulated 
    implementation is from the 'basksim' package, which was developed for
    Baumann et al. (2024) <doi:10.1080/19466315.2024.2402275>. The package's 
    syntax is compatible with the 'basksim' syntax and easily extendable.

Bayesian fitting and sensitivity analysis methods for adaptive
    spline surfaces described in <doi:10.18637/jss.v094.i08>. Built to handle continuous and categorical inputs as well as
    functional or scalar output. An extension of the methodology in Denison, Mallick
    and Smith (1998) <doi:10.1023/A:1008824606259>.