Series of partially balanced incomplete block designs (PBIB) based on the combinatory method (S) introduced in (Imane Rezgui et al, 2014) <doi:10.3844/jmssp.2014.45.48>; and it gives their associated U-type design.

Provides functions and a workflow to easily and powerfully calculating specificity, sensitivity and ROC curves of biomarkers combinations. Allows to rank and select multi-markers signatures as well as to find the best performing sub-signatures, now also from single-cell RNA-seq datasets. The method used was first published as a Shiny app and described in Mazzara et al. (2017) <doi:10.1038/srep45477> and further described in Bombaci & Rossi (2019) <doi:10.1007/978-1-4939-9164-8_16>, and widely expanded as a package as presented in the bioRxiv pre print Ferrari et al. <doi:10.1101/2022.01.17.476603>.

Provides iterators for combinations, permutations, subsets, and
    Cartesian product, which allow one to go through all elements without creating a
    huge set of all possible values.

Use frequentist and Bayesian methods to estimate parameters from a
  binary outcome misclassification model. These methods correct for the problem
  of "label switching" by assuming that correct outcome classification occurs
  in at least 50% of observations. A description of the analysis methods is
  available in Hochstedler and Wells (2023) <arXiv:2303.10215>.

Computes community climate statistics for volume and mismatch using species' climate niches either unscaled or scaled relative to a regional species pool. These statistics can be used to describe biogeographic patterns and infer community assembly processes. Includes a vignette outlining usage.

An algorithm for identifying combinations of mutually exclusive alterations in cancer genomes. CoMEt represents the mutations in a set M of k genes with a 2^k dimensional contingency table, and then computes the tail probability of observing T(M) exclusive alterations using an exact statistical test.

A convenient 'R' wrapper to the 'Comet' API, which is a cloud
    platform allowing you to track, compare, explain and optimize machine
    learning experiments and models. Experiments can be viewed on the 'Comet'
    online dashboard at <https://www.comet.com>.