To find the certainty of dominance interactions with indirect
    interactions being considered.

An implementation of two functions that estimate values for percentiles from an ordered categorical variable as described by Reardon (2011, isbn:978-0-87154-372-1). One function estimates percentile differences from two percentiles while the other returns the values for every percentile from 1 to 100.

Several person-fit statistics 
  (PFSs; Meijer and Sijtsma, 2001, <doi:10.1177/01466210122031957>) 
  are offered. These statistics allow assessing whether
  individual response patterns to tests or questionnaires are (im)plausible given 
  the other respondents in the sample or given a specified item response theory model. Some PFSs apply to 
  dichotomous data, such as the likelihood-based PFSs (lz, lz*) and the group-based PFSs 
  (personal biserial correlation, caution index, (normed) number of Guttman errors, 
  agreement/disagreement/dependability statistics, U3, ZU3, NCI, Ht). PFSs suitable to polytomous data include
  extensions of lz, U3, and (normed) number of Guttman errors.

Implementation of different performance measures for classification and ranking tasks  including  Area Under the Receiving Characteristic Curve (AUROC) and Area Under the Precision Recall Curve (AUPRC), precision at a given recall, F-score for single and multiple classes.

Utilities for computing measures to assess model quality,
    which are not directly provided by R's 'base' or 'stats' packages.
    These include e.g. measures like r-squared, intraclass correlation
    coefficient (Nakagawa, Johnson & Schielzeth (2017)
    <doi:10.1098/rsif.2017.0213>), root mean squared error or functions to
    check models for overdispersion, singularity or zero-inflation and
    more. Functions apply to a large variety of regression models,
    including generalized linear models, mixed effects models and Bayesian
    models. References: Lüdecke et al. (2021) <doi:10.21105/joss.03139>.

Collection of econometric functions for performance and risk 
    analysis. In addition to standard risk and performance metrics, this 
    package aims to aid practitioners and researchers in utilizing the latest
    research in analysis of non-normal return streams.  In general, it is most 
    tested on return (rather than price) data on a regular scale, but most 
    functions will work with irregular return data as well, and increasing
    numbers of functions will work with P&L or price data where possible.