Estimate and understand individual-level variation in
    transmission. Implements density and cumulative compound Poisson
    discrete distribution functions (Kremer et al. (2021) 
    <doi:10.1038/s41598-021-93578-x>), as well as functions to calculate
    infectious disease outbreak statistics given epidemiological
    parameters on individual-level transmission; including the probability
    of an outbreak becoming an epidemic/extinct (Kucharski et al. (2020) 
    <doi:10.1016/S1473-3099(20)30144-4>), or the cluster size
    statistics, e.g. what proportion of cases cause X\% of transmission
    (Lloyd-Smith et al. (2005) <doi:10.1038/nature04153>).

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>).

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.

The functions sp() and sp_seq() compute the support points in Mak and Joseph (2018) <DOI:10.1214/17-AOS1629>. Support points can be used as a representative sample of a desired distribution, or a representative reduction of a big dataset (e.g., an "optimal" thinning of Markov-chain Monte Carlo sample chains). This work was supported by USARO grant W911NF-14-1-0024 and NSF DMS grant 1712642.

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.