Provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for: 1) fitting linear mixed models, 2) constructing marker-based genomic relationship matrices, 3) estimating genetic parameters (heritability and correlation), 4) performing genomic prediction and genetic risk profiling, and 5) single or multi-marker association analyses.
    Rohde et al. (2019) <doi:10.1101/503631>.

Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.

Provides seamless access to the 'QGIS'
    (<https://qgis.org>) processing toolbox using the standalone
    'qgis_process' command-line utility.  Both native and third-party
    (plugin) processing providers are supported.  Beside referring data
    sources from file, also common objects from 'sf', 'terra' and 'stars'
    are supported. The native processing algorithms are documented by QGIS.org 
    (2024) <https://docs.qgis.org/latest/en/docs/user_manual/processing_algs/>.

Fork of qgraph - Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>.

A 'shiny' application for teaching introductory quantitative genetics
    and plant breeding through interactive simulations. The application relies on 
    established plant breeding and quantitative genetic theory found in 
    Falconer and Mackay (1996, ISBN:0582243025) and Bernardo (2010, ISBN:978-0972072427).

This function produces both the numerical and graphical summaries of the QTL hotspot detection in the genomes that are available on the worldwide web including the flanking markers of QTLs.

Nomograms are constructed to predict the cumulative incidence
    rate which is calculated after adjusting for competing causes to the event of interest. 
    K-fold cross-validation is implemented to validate predictive accuracy using a competing-risk version of the concordance index.
    Methods are as described in: Kattan MW, Heller G, 
    Brennan MF (2003).