An opinionated framework for building a production-ready
    'Shiny' application. This package contains a series of tools for
    building a robust 'Shiny' application from start to finish.

An efficient algorithm to generate group assignments for classroom settings while minimizing repeated pairings across multiple rounds.

In gene-expression microarray studies, for example, one generally obtains a list of
 dozens or hundreds of genes that differ in expression between samples and then asks
 'What does all of this mean biologically?' Alternatively, gene lists can be derived conceptually
 in addition to experimentally. For instance, one might want to analyze a group of genes known
 as housekeeping genes. The work of the Gene Ontology (GO) Consortium <geneontology.org>
 provides a way to address that question. GO organizes genes into hierarchical categories based
 on biological process, molecular function and subcellular localization. The role of 'GoMiner'
 is to automate the mapping between a list of genes and GO, and to provide a statistical
 summary of the results as well as a visualization.

A zero-inflated quasi-Poisson factor model to display similarity between samples visually in a low (2 or 3) dimensional space.

Allows the user to animate text within 'rmarkdown' documents and 'shiny' applications. 
    The animations are activated using the 'Animate.css' library. See <https://animate.style/> for more information.

The gamma-Orthogonal Matching Pursuit (gamma-OMP) is a recently suggested modification of the OMP feature selection algorithm for a wide range of response variables. The package offers many alternative regression models, such linear, robust, survival, multivariate etc., including k-fold cross-validation. References: Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2018). "Efficient feature selection on gene expression data: Which algorithm to use?" BioRxiv. <doi:10.1101/431734>. Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2022). "The gamma-OMP algorithm for feature selection with application to gene expression data". IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2): 1214--1224. <doi:10.1109/TCBB.2020.3029952>.