Functions are available to calibrate designs over a range of posterior and predictive thresholds, to plot the various design options, and to obtain the operating characteristics of optimal accuracy and optimal efficiency designs.

This is an implementation of the partial profile score feature 
    selection (PPSFS) approach to generalized linear (interaction) models. 
    The PPSFS is highly scalable even for ultra-high-dimensional feature space. 
    See the paper by Xu, Luo and Chen (2021, <doi:10.4310/21-SII706>).

The Predictive Power Score (PPS) is an asymmetric, data-type-agnostic score 
    that can detect linear or non-linear relationships between two variables. 
    The score ranges from 0 (no predictive power) to 1 (perfect predictive power). 
    PPS can be useful for data exploration purposes, in the same way correlation analysis is.
    For more information on PPS, see <https://github.com/paulvanderlaken/ppsr>.

Provides functionality for the prior and posterior projected Polya tree for the analysis of circular data 
  (Nieto-Barajas and Nunez-Antonio (2019) <arXiv:1902.06020>).

It was developed as a tool for exploring 'PPTreereg' (Projection Pursuit TREE of REGression). 
  It uses various projection pursuit indexes and 'XAI' (eXplainable Artificial Intelligence) methods to help 
  understand the model by finding connections between the input variables and prediction values of the model. 
  The 'KernelSHAP' (Aas, Jullum and Løland (2019) <arXiv:1903.10464>) algorithm was modified to fit ‘PPTreereg’, 
  and some codes were modified from the 'shapr' package (Sellereite, Nikolai, and Martin Jullum (2020) <doi:10.21105/joss.02027>). 
  The implemented methods help to explore the model at the single instance level as well as at the whole dataset level. 
  Users can compare with other machine learning models by applying it to the 'DALEX' package of 'R'.

Tools for exploring projection pursuit classification tree using
    various projection pursuit indexes.

This package allows users to calculate the number of
    under-five child deaths caused by consumption of poor quality
    antimalarials across 39 sub-Saharan nations. The package supports one
    function, that starts an interactive web tool created using
    the shiny R package. The web tool runs locally on the user's machine.
    The web tool allows users to set input parameters (prevalence of poor
    quality antimalarials, case fatality rate of children who take poor
    quality antimalarials, and sample size) which are then used to perform
    an uncertainty analysis following the Latin hypercube
    sampling scheme. Users can download the output figures as PDFs, and the
    output data as CSVs. Users can also download their input parameters
    for reference. This package was designed to accompany the analysis
    presented in:
    J. Patrick Renschler, Kelsey Walters, Paul Newton, Ramanan Laxminarayan
    "Estimated under-five deaths associated with poor-quality
    antimalarials in sub-Saharan Africa", 2014. Paper submitted.