Normally building a GODB is fairly complicated, involving downloading 
 multiple database files and using these to build e.g. a 'mySQL' database. 
 Accessing this database is also complicated, involving an intimate knowledge
 of the database in order to construct reliable queries.
 Here we have a more modest goal, generating GOGOA3, which is a stripped down
 version of the GODB that 
 was originally restricted to human genes as designated by the HUGO Gene Nomenclature
 Committee (HGNC) (see <https://geneontology.org/>). I have now added about two dozen
 additional species, namely all species represented on the Gene Ontology
 download page <https://current.geneontology.org/products/pages/downloads.html>.
 This covers most of the model organisms that are commonly used in bio-medical
 and basic research (assuming that anyone still has a grant to do such research).
 This can be built in a matter of seconds from 2 easily
 downloaded files (see <https://current.geneontology.org/products/pages/downloads.html>
 and <https://geneontology.org/docs/download-ontology/>), and it can be queried by e.g.
 w<-which(GOGOA3[,"HGNC"] %in% hgncList) where GOGOA3
 is a matrix representing the minimalist GODB and hgncList is a list of
 gene identifiers. This database will be used in my upcoming package 'GoMiner'
 which is based on my previous publication (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003)<doi:10.1186/gb-2003-4-4-r28>).
 Relevant .RData files are available from GitHub (<https://github.com/barryzee/GO/tree/main/databases>).

Generate central composite designs (CCD)with full as well 
    as fractional factorial points (half replicate) and Box Behnken 
    designs (BBD) with minimally changed run sequence.

Create tile grid maps, which are like choropleth maps except each
  region is represented with equal visual space.

The minimax family of distributions is a two-parameter
        family like the beta family, but computationally a lot more
        tractible.

A computationally efficient solution for generating optimal experimental designs in Accelerated Life Testing (ALT). Leveraging a Particle Swarm Optimization (PSO)-based hybrid algorithm, the package identifies optimal test plans that minimize estimation variance under specified failure models and stress profiles. For more detailed, see Lee et al. (2025), Optimal Robust Strategies for Accelerated Life Tests and Fatigue Testing of Polymer Composite Materials, submitted to Annals of Applied Statistics, <https://imstat.org/journals-and-publications/annals-of-applied-statistics/annals-of-applied-statistics-next-issues/>, and Hoang (2025), Model-Robust Minimax Design of Accelerated Life Tests via PSO-based Hybrid Algorithm, Master' Thesis, Unpublished.