Accurate calculations and visualization of precision-recall and ROC (Receiver Operator Characteristics)
    curves. Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.

Fornece acesso eficiente à malha espacial de precariedade
    viária brasileira. O pacote realiza o download em cache e a
    leitura otimizada (via Apache Arrow) de arquivos Parquet
    particionados, contendo o cruzamento de variáveis de
    infraestrutura do Entorno do Censo Demográfico 2022 (IBGE)
    com a malha viária aberta do Overture Maps.
    [English] Provides efficient access to the spatial network of
    road precariousness in Brazil. The package performs cached
    downloads and optimized reading (via Apache Arrow) of partitioned
    Parquet files. These files contain the intersection of infrastructure
    variables from the 2022 Demographic Census (IBGE) with the open
    street network from Overture Maps. Methodology and datasets are
    detailed in Passos (2026) <doi:10.5281/zenodo.19711448>.

Based on different statistical definitions of discrimination, several methods have been proposed to detect and mitigate social inequality in machine learning models. 
  This package aims to provide an alternative to fairness treatment in predictive models. The ROC method implemented in this package
  is described by Kamiran, Karim and Zhang (2012) <https://ieeexplore.ieee.org/document/6413831/>.

Performs genomic prediction of hybrid performance using eight statistical methods including GBLUP, BayesB, RKHS, PLS, LASSO, EN, LightGBM and XGBoost along with additive and additive-dominance models. Users are able to incorporate parental phenotypic information in all methods based on their specific needs. (Xu S et al(2017) <doi:10.1534/g3.116.038059>; Xu Y et al (2021) <doi: 10.1111/pbi.13458>).

Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, XGBoost and LightGBM.
             GBLUP: genomic best liner unbiased prediction, RKHS: reproducing kernel Hilbert space, PLS: partial least squares regression, LASSO: least absolute shrinkage and selection operator, XGBoost: extreme gradient boosting, LightGBM: light gradient boosting machine.
             It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) <doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>).
			 A complete manual for this package is provided in the manual folder of the package installation directory. 
			 You can locate the manual by running the following command in R: system.file("manual", package = "predhy.GUI").