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Title: Tools for Creating Universal Numeric Fingerprints for Data
Authors@R: c(person("Thomas", "Leeper", role = c("aut","cre"),
person("Micah", "Altman", role = c("aut")))
Description: Computes a universal numeric fingerprint (UNF) for an R data object. UNF is a cryptographic hash or signature that can be used to uniquely identify (a version of) a rectangular dataset, or a subset thereof. UNF can be used, in tandem with a DOI, to form a persistent citation to a versioned dataset.

Description: Computes a universal numeric fingerprint (UNF) for an R data
object. UNF is a cryptographic hash or signature that can be used to uniquely
identify (a version of) a rectangular dataset, or a subset thereof. UNF can
be used, in tandem with a DOI, to form a persistent citation to a versioned
Imports: utils, stats, tools, base64enc, digest

Author: Thomas J. Leeper [aut, cre] (<https://orcid.org/0000-0003-4097-6326>),

Description: Computes a 'universal numeric fingerprint' ('UNF') for an R data
object. 'UNF' is a hash or signature that can be used to uniquely
identify (a version of) a rectangular dataset, or a subset thereof. 'UNF' can
be used, in tandem with a 'DOI', to form a persistent citation to a versioned

Title: Non-Parametric Cluster Significance Testing with Reference to a
Author: Erika S. Helgeson, David Vock, and Eric Bair
Description: Assess the significance of identified clusters and estimates the true number of clusters by comparing the explained variation due to the clustering from the original data to that produced by clustering a unimodal reference distribution which preserves the covariance structure in the data. The reference distribution is generated using kernel density estimation and a Gaussian copula framework. A dimension reduction strategy and sparse covariance estimation optimize this method for the high-dimensional, low-sample size setting. This method is similar to them method described in Helgeson and Bair (2016) <arXiv:1610.01424> except a Gaussian copula approach is used to account for feature correlation.