Filtering, also known as gating, of flow cytometry samples using 
             the curvHDR method, which is described in Naumann, U., Luta, G. and 
             Wand, M.P. (2010) <DOI:10.1186/1471-2105-11-44>.

Automatic specification and estimation of reserve demand curves for central bank operations. The package can help to choose the best demand curve and identify additional explanatory variables. Various plot and predict options are included. For more details, see Chen et al. (2023) <https://www.imf.org/en/Publications/WP/Issues/2023/09/01/Modeling-the-Reserve-Demand-to-Facilitate-Central-Bank-Operations-538754>.

Cobb's maximum likelihood method for cusp-catastrophe modeling
        (Grasman, van der Maas, and Wagenmakers (2009) <doi:10.18637/jss.v032.i08>;
        Cobb (1981), Behavioral Science, 26(1), 75-78). Includes a cusp() function for model 
        fitting, and several utility functions for plotting, and for comparing the
        model to linear regression and logistic curve models.

Use the high-precision arithmetic provided by the R package 'Rmpfr' 
    to compute a custom-made Gauss quadrature nodes and weights, 
    with up to 33 nodes, using a moment-based method via moment 
    determinants. Paul Kabaila (2022) <arXiv:2211.04729>.

A versatile R package for creating and pricing custom derivatives to suit your financial needs.

Functions for evaluating and visualizing predictive model performance (specifically: binary classifiers) in the field of customer scoring. These metrics include lift, lift index, gain percentage, top-decile lift, F1-score, expected misclassification cost and absolute misclassification cost. See Berry & Linoff (2004, ISBN:0-471-47064-3), Witten and Frank (2005, 0-12-088407-0) and Blattberg, Kim & Neslin (2008, ISBN:978–0–387–72578–9) for details. Visualization functions are included for lift charts and gain percentage charts. All metrics that require class predictions offer the possibility to dynamically determine cutoff values for transforming real-valued probability predictions into class predictions.