Functions and examples based on the m-out-of-n bootstrap suggested by
  Politis, D.N. and Romano, J.P. (1994) <doi:10.1214/aos/1176325770>.
  Additionally there are functions to estimate the scaling factor tau
  and the subsampling size m. For a detailed description and a full list
  of references, see Dalitz, C. and Lögler, F. (2025)
  <doi:10.32614/RJ-2025-031>.

Visualization of multi-dimensional data arising in multi-objective optimization, including plots of the empirical attainment function (EAF), M. López-Ibáñez, L. Paquete, and T. Stützle (2010) <doi:10.1007/978-3-642-02538-9_9>, and symmetric Vorob'ev expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) <doi:10.1016/j.ejor.2014.07.032>, among others.

Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) <doi:10.48550/arXiv.2209.01493>.  It consumes as inputs the lm object from an estimated OLS regression (based on the "training sample") and a data.frame of out-of-sample cases (the "test sample") that have non-missing values for the same predictors. The test sample may or may not include data on the outcome variable; if it does, that variable is not used. The aim of the exercise is to project what what mean squared out-of-sample error can be expected given the predictor values supplied in the test sample. Output consists of a list of three elements: the projected mean squared out-of-sample error, the projected out-of-sample R-squared, and a vector of out-of-sample "hat" or "leverage" values, as defined in the paper.

A set of tools to perform multiple versions of the Mobility
    Oriented-Parity metric. This multivariate analysis helps to characterize 
    levels of dissimilarity between a set of conditions of reference and another
    set of conditions of interest. If predictive models are transferred to 
    conditions different from those over which models were calibrated (trained),
    this metric helps to identify transfer conditions that differ 
    substantially from those of calibration. These tools are implemented 
    following principles proposed in Owens et al. (2013) 
    <doi:10.1016/j.ecolmodel.2013.04.011>, and expanded to obtain more detailed 
    results that aid in interpretation as in Cobos et al. (2024)
    <doi:10.21425/fob.17.132916>.