This is a data-only package to provide example data for
  other packages that are part of the "Object-Oriented Microrray and
  Proteomics Analysis" suite of packages. These are described in more
  detail at the package URL.

Implementation of optimistic optimization methods for global optimization of deterministic or stochastic functions. The algorithms feature guarantees of the convergence to a global optimum. They require minimal assumptions on the (only local) smoothness, where the smoothness parameter does not need to be known. They are expected to be useful for the most difficult functions when we have no information on smoothness and the gradients are unknown or do not exist. Due to the weak assumptions, however, they can be mostly effective only in small dimensions, for example, for hyperparameter tuning.

Provides functions for estimation under the Randomised Response Technique for sensitive survey data, including Warner's estimator, Optional Randomised Response Technique estimator proposed by Chaudhuri and Mukerjee,and the Optimized Optional Randomised Response Technique estimator proposed by Pushadapu et al. The package also includes Monte Carlo simulation tools for evaluating estimator performance. The implemented methods are based on Warner (1965) <doi:10.1080/01621459.1965.10480775>, Chaudhuri and Mukerjee (1985),and Pushadapu et al. (2025) <doi: 10.1111/insr.12581>.

Estimates out-of-sample R² through bootstrap or cross-validation as a measure of predictive performance. In addition, a standard error for this point estimate is provided, and confidence intervals are constructed.

Implements the out-of-treatment testing from Kuelpmann and Kuzmics (2020) <doi:10.2139/ssrn.3441675> based on the Vuong Test introduced in Vuong (1989) <doi:10.2307/1912557>. 
    Out-of treatment testing allows for a direct, pairwise likelihood comparison of theories, calibrated with pre-existing data.

Quantifies hypothesis to data fit for repeated measures 
    and longitudinal data, as described by Thorngate (1987) 
    <doi:10.1016/S0166-4115(08)60083-7> and Grice et al., (2015) 
    <doi:10.1177/2158244015604192>. Hypothesis and data are encoded as
    pairwise relative orderings which are then compared to determine the
    percentage of orderings in the data that are matched by the hypothesis.