The Pearson-ICA algorithm is a mutual information-based
        method for blind separation of statistically independent source
        signals. It has been shown that the minimization of mutual
        information leads to iterative use of score functions, i.e.
        derivatives of log densities. The Pearson system allows
        adaptive modeling of score functions. The flexibility of the
        Pearson system makes it possible to model a wide range of
        source distributions including asymmetric distributions. The
        algorithm is designed especially for problems with asymmetric
        sources but it works for symmetric sources as well.

Provides a probabilistic framework that integrates Data Envelopment
  Analysis (DEA) (Banker et al., 1984) <doi:10.1287/mnsc.30.9.1078> with machine
  learning classifiers (Kuhn, 2008) <doi:10.18637/jss.v028.i05> to estimate both the
  (in)efficiency status and the probability of efficiency for decision-making
  units. The approach trains predictive models on DEA-derived efficiency labels
  (Charnes et al., 1985) <doi:10.1016/0304-4076(85)90133-2>, enabling explainable
  artificial intelligence (XAI) workflows with global and local interpretability
  tools, including permutation importance (Molnar et al., 2018) <doi:10.21105/joss.00786>,
  Shapley value explanations (Strumbelj & Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>,
  and sensitivity analysis (Cortez, 2011) <https://CRAN.R-project.org/package=rminer>.
  The framework also supports probability-threshold peer selection and counterfactual
  improvement recommendations for benchmarking and policy evaluation. The probabilistic
  efficiency framework is detailed in González-Moyano et al. (2025)
  "Probability-based Technical Efficiency Analysis through Machine Learning",
  in review for publication.

Validation of risk predictions obtained from survival models and
    competing risk models based on censored data using inverse weighting and
    cross-validation. Most of the 'pec' functionality has been moved to 'riskRegression'.