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.

Algorithms for D-, A-, I-, and c-optimal designs. For more details, see the package description. Some of the functions in this package require the 'gurobi' software and its accompanying R package. For their installation, please follow the instructions at <https://www.gurobi.com> and the file gurobi_inst.txt, respectively.

Functions for the design process of survey sampling, with specific tools for multi-wave and multi-phase designs. Perform optimum allocation using Neyman (1934) <doi:10.2307/2342192> or Wright (2012) <doi:10.1080/00031305.2012.733679> allocation, split strata based on quantiles or values of known variables, randomly select samples from strata, allocate sampling waves iteratively, and organize a complex survey design. Also includes a Shiny application for observing the effects of different strata splits. A paper on this package was published in the Journal of Statistical Software <doi:10.18637/jss.v114.i10>.