Provision of classes and methods for estimating generalized
   orthogonal GARCH models. This is an alternative approach to CC-GARCH models
   in the context of multivariate volatility modeling.

Tools for fitting statistical network models to dynamic network data.
   Can be used for fitting both dynamic network actor models ('DyNAMs') and
   relational event models ('REMs').
   Stadtfeld, Hollway, and Block (2017a) <doi:10.1177/0081175017709295>,
   Stadtfeld, Hollway, and Block (2017b) <doi:10.1177/0081175017733457>,
   Stadtfeld and Block (2017) <doi:10.15195/v4.a14>,
   Hoffman et al. (2020) <doi:10.1017/nws.2020.3>.

Implements the Goldilocks adaptive trial design for a time to event
    outcome using a piecewise exponential model and conjugate Gamma prior
    distributions. The method closely follows the article by Broglio and
    colleagues <doi:10.1080/10543406.2014.888569>, which allows users to explore
    the operating characteristics of different trial designs.

A collection of gold price data in various currencies in the form of USD, EUR, JPY, GBP, CAD, CHF, INR, CNY, TRY, SAR, IDR, AED, THB, VND, EGP, KRW, RUB, ZAR, and AUD. This data comes from the World Gold Council. In addition, the data is in the form of daily, weekly, monthly (average and the end of period), quarterly (average and the end of period), and yearly (average and the end of period).

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.