Our recently developed fully robust Bayesian semiparametric mixed-effect model for high-dimensional longitudinal studies with heterogeneous observations can be implemented through this package. This model can distinguish between time-varying interactions and constant-effect-only cases to avoid model misspecifications. Facilitated by spike-and-slab priors, this model leads to superior performance in estimation, identification and statistical inference. In particular, robust Bayesian inferences in terms of valid Bayesian credible intervals on both parametric and nonparametric effects can be validated on finite samples. The Markov chain Monte Carlo algorithms of the proposed and alternative models are efficiently implemented in 'C++'.

Create a blended curve from two survival curves, which is particularly useful
          for survival extrapolation in health technology assessment. The main idea is to
          mix a flexible model that fits the observed data well with a parametric model that
          encodes assumptions about long-term survival. The two curves are blended into a
          single survival curve that is identical to the first model over the range of 
          observed times and gradually approaches the parametric model over the extrapolation
          period based on a given weight function. This approach allows for the inclusion of
          external information, such as data from registries or expert opinion, to guide
          long-term extrapolations, especially when dealing with immature trial data.
          See Che et al. (2022) <doi:10.1177/0272989X221134545>.

Colour blend functions. These functions make it easier to blend 
    colours and palettes using digital blend modes such as multiply, screen, 
    and overlay.

Performs a joint analysis of experiments with mixtures and random effects, taking on a process variable represented by a covariable.

Computation of key characteristics and plots for blinded sample size recalculation.
   Continuous as well as binary endpoints are supported in superiority and non-inferiority trials.
   See Baumann, Pilz, Kieser (2022) <doi:10.32614/RJ-2022-001> for a detailed description.
   The implemented methods include the approaches by
   Lu, K. (2016) <doi:10.1002/pst.1737>,
   Kieser, M. and Friede, T. (2000) <doi:10.1002/(SICI)1097-0258(20000415)19:7%3C901::AID-SIM405%3E3.0.CO;2-L>,
   Friede, T. and Kieser, M. (2004) <doi:10.1002/pst.140>, 
   Friede, T., Mitchell, C., Mueller-Veltern, G. (2007) <doi:10.1002/bimj.200610373>, and
   Friede, T. and Kieser, M. (2011) <doi:10.3414/ME09-01-0063>.