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 (2022, <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>).

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

Weighted Deming regression, also known as "errors-in-variable" 
    regression, is applied with suitable weights. Weights are modeled via a 
    precision profile; functions are provided for implementing it in both known 
    and unknown precision profile situations. The package provides tools for 
    precision profile weighted Deming (PWD) regression. 
    It covers two settings – one where the precision profiles are known either 
    from external studies or from adequate replication of the X and Y readings, 
    and one in which there is a plausible functional form for the precision 
    profiles but the exact function must be estimated from the (generally 
    singlicate) readings. 
    The function set includes tools for: estimated standard errors (via 
    jackknifing); standardized-residual analysis function with regression 
    diagnostic tools for normality, linearity and constant variance; and an 
    outlier analysis identifying significant outliers for closer investigation.
    Further information on mathematical derivations and applications can be 
    found on arXiv: Hawkins and Kraker (2025) <doi:10.48550/arXiv.2508.02888>.