Likelihood based optimal partitioning and indicator
  species analysis. Finding the best binary partition for each species
  based on model selection, with the possibility to take into account
  modifying/confounding variables as described
  in Kemencei et al. (2014) <doi:10.1556/ComEc.15.2014.2.6>.
  The package implements binary and multi-level response models,
  various measures of uncertainty, Lorenz-curve based thresholding,
  with native support for parallel computations.

A single function 'options.ifunset(...)' is contained herewith, which allows the user to set a global option ONLY if it is not already set. By this token, for package maintainers this function can be used in preference to the standard 'options(...)' function, making provision for THEIR end user to place 'options(...)' directives within their '.Rprofile' file, which will not be overridden at the point when a package is loaded.

A set of standard benchmark optimization functions for R and
    a common interface to sample them.

High-performance implementation of 36 optimal binning algorithms 
    (16 categorical, 20 numerical) for Weight of Evidence ('WoE') transformation, 
    credit scoring, and risk modeling. Includes advanced methods such as Mixed 
    Integer Linear Programming ('MILP'), Genetic Algorithms, Simulated Annealing, 
    and Monotonic Regression. Features automatic method selection based on 
    Information Value ('IV') maximization, strict monotonicity enforcement, and 
    efficient handling of large datasets via 'Rcpp'. Fully integrated with the 
    'tidymodels' ecosystem for building robust machine learning pipelines. 
    Based on methods described in Siddiqi (2006) <doi:10.1002/9781119201731> 
    and Navas-Palencia (2020) <doi:10.48550/arXiv.2001.08025>.

Computes optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained.