Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm.  
 For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) <arXiv:1911.00658>. 
 This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. 
 Further study is going on for the nonorthogonal cases and generalized linear models. 
 These works are in part supported by the National Natural Foundation of China (No.12171076).

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.

Tools for using genetic markers, stable isotope data, and habitat
    suitability data to calculate posterior probabilities of breeding origin of
    migrating birds.

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.