Functions and data sets reproducing some examples in
             Box, Hunter and Hunter II.  Useful for statistical design
             of experiments, especially factorial experiments.

Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models and continuous threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method.

Provides functions for the evaluation of basket
    trial designs with binary endpoints. Operating characteristics of a
    basket trial design are assessed by simulating trial data according to
    scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and
    assessing decision probabilities on stratum and trial-level based on Go / No-go decision making.
    The package is build for high flexibility regarding decision rules,
    number of interim analyses, number of strata, and recruitment.
    The BHMs proposed by
    Berry et al. (2013) <doi:10.1177/1740774513497539>
    and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>,
    as well as a model that combines both approaches are implemented.
    Functions are provided to implement Bayesian decision rules as for example
    proposed by Fisch et al. (2015) <doi:10.1177/2168479014533970>.
    In addition, posterior point estimates (mean/median) and credible intervals
    for response rates and some model parameters can be calculated.
    For simulated trial data, bias and mean squared errors of posterior
    point estimates for response rates can be provided.

Package BHMSMAfMRI performs Bayesian hierarchical multi-subject multiscale analysis of fMRI data as described in Sanyal & Ferreira (2012) <DOI:10.1016/j.neuroimage.2012.08.041>, or other multiscale data, using wavelet-based prior that borrows strength across subjects and provides posterior smoothed images of the effect sizes and samples from the posterior distribution.