Tools for analysing the agreement of two or more rankings of the same items. Examples are importance rankings of predictor variables and risk predictions of subjects. Benchmarks for agreement are computed based on random permutation and bootstrap. See Ekstrøm CT, Gerds TA, Jensen, AK (2018). "Sequential rank agreement methods for comparison of ranked lists." _Biostatistics_, *20*(4), 582-598 <doi:10.1093/biostatistics/kxy017> for more information.

The Patient Rule Induction Method (PRIM) is typically
  used for "bump hunting" data mining to identify regions with abnormally
  high concentrations of data with large or small values. This package
  extends this methodology so that it can be applied to binary classification
  problems and used for prediction.

Calculates the sup MZ value to detect the unknown structural break points under Heteroskedasticity as given in Ahmed et al. (2017) (<DOI: 10.1080/03610926.2016.1235200>).

Downloads data supplementary materials from manuscripts,
    using papers' DOIs as references. Facilitates open, reproducible
    research workflows: scientists re-analyzing published datasets can
    work with them as easily as if they were stored on their own
    computer, and others can track their analysis workflow
    painlessly. The main function suppdata() returns a (temporary)
    location on the user's computer where the file is stored, making
    it simple to use suppdata() with standard functions like
    read.csv().

Ten distributions supplementing those built into R.
  Inverse Gauss, Kruskal-Wallis, Kendall's Tau, Friedman's chi
  squared, Spearman's rho, maximum F ratio, the Pearson product
  moment correlation coefficient, Johnson distributions, normal
  scores and generalized hypergeometric distributions.

Provides basic functions that support an implementation of object case (Case 1) best-worst scaling: a function for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; two functions for creating a data set suitable for analysis; a function for calculating count-based scores; a function for calculating shares of preference; and a function for generating artificial responses to questions. See Louviere et al. (2015) <doi:10.1017/CBO9781107337855> for details on best-worst scaling, and Aizaki and Fogarty (2023) <doi:10.1016/j.jocm.2022.100394> for the package.