Generate boilerplate code for R6 classes. Given R6 class create getters 
    and/or setters for selected class fields or use RStudio addins to insert methods 
    straight into class definition.

Build robust and maintainable software with object-oriented
    design patterns in R. Design patterns abstract and present in neat,
    well-defined components and interfaces the experience of many software
    designers and architects over many years of solving similar problems.
    These are solutions that have withstood the test of time with respect
    to re-usability, flexibility, and maintainability. 'R6P' provides
    abstract base classes with examples for a few known design patterns.
    The patterns were selected by their applicability to analytic projects
    in R. Using these patterns in R projects have proven effective in
    dealing with the complexity that data-driven applications possess.

A comprehensive suite of statistical tools for Quality Management, designed around the Define, Measure, Analyze, Improve, and Control (DMAIC) cycle used in Six Sigma methodology. Based on the discontinued CRAN package 'qualitytools', this package refactors its original design by incorporating 'R6' object-oriented programming for increased flexibility and performance. It replaces traditional graphics with modern, interactive visualizations using 'ggplot2' and 'plotly'. Built on 'tidyverse' principles, it simplifies data manipulation and visualization, offering an intuitive approach to quality science.

Functionality for performing a principled reference analysis in the Bayesian normal-normal hierarchical model used for Bayesian meta-analysis, as described in Ott, Plummer and Roos (2021) <doi:10.1002/sim.9076>. Computes a reference posterior, induced by a minimally informative improper reference prior for the between-study (heterogeneity) standard deviation. Determines additional proper anti-conservative (and conservative) prior benchmarks. Includes functions for reference analyses at both the posterior and the prior level, which, given the data, quantify the informativeness of a heterogeneity prior of interest relative to the minimally informative reference prior and the proper prior benchmarks. The functions operate on data sets which are compatible with the 'bayesmeta' package.