Performs genetic algorithm (Scrucca, L (2013) <doi:10.18637/jss.v053.i04>) 
             assisted genomic best liner unbiased prediction for genomic selection.
             It also provides a binning method in natural population for genomic selection
             under the principle of linkage disequilibrium for dimensional reduction.

Procedures for calculating variance components, study variation, percent study variation, and percent tolerance for gauge repeatability and reproducibility study.  Methods included are ANOVA and Average / Range methods.  Requires balanced study.

Provides an effective machine learning-based tool that quantifies the gain of passive device installation on wind turbine generators.
  H. Hwangbo, Y. Ding, and D. Cabezon (2019) <arXiv:1906.05776>.

Constructs gains tables and lift charts for prediction algorithms. Gains tables and lift charts are commonly used in direct marketing applications.  The method is described in Drozdenko and Drake (2002), "Optimal Database Marketing", Chapter 11.

Implements graphical extension with accuracy in parameter estimation (AIPE) on RMSEA for sample size planning in structural equation modeling based on Lin, T.-Z. & Weng, L.-J. (2014) <doi: 10.1080/10705511.2014.915380>. And, it can also implement AIPE on RMSEA and power analysis on RMSEA.

The Global Biodiversity Information Facility 
    ('GBIF', <https://www.gbif.org>) sources data from an international network
    of data providers, known as 'nodes'. Several of these nodes - the "living 
    atlases" (<https://living-atlases.gbif.org>) - maintain their own web 
    services using software originally developed by the Atlas of Living 
    Australia ('ALA', <https://www.ala.org.au>). 'galah' enables the R community 
    to directly access data and resources hosted by 'GBIF' and its partner nodes.

Implements the GALAHAD algorithm (Geometry-Adaptive 
    'Lyapunov'-Assured Hybrid Optimizer), combining 'Riemannian' metrics, 
    'Lyapunov' stability checks, and trust-region methods for stable 
    optimization of mixed-geometry parameters. Designed for biological 
    modeling (germination, dose-response, survival) where rates, 
    concentrations, and unconstrained variables coexist. Developed at 
    the Minnesota Center for Prion Research and Outreach (MNPRO), 
    University of Minnesota. Based on Conn et al. (2000) 
    <doi:10.1137/1.9780898719857>, Amari (1998) 
    <doi:10.1162/089976698300017746>, Beck & Teboulle (2003) 
    <doi:10.1016/S0167-6377(02)00231-6>, Nesterov (2017) 
    <https://www.jstor.org/stable/resrep30722>, and Walne et al. (2020) 
    <doi:10.1002/agg2.20098>.