Statistical functions to fit, validate and describe a Generalized
    Waring Regression Model (GWRM).

Computes the sample probability value (p-value)
    for the estimated coefficient from a standard
    genome-wide univariate regression.  It computes the exact
    finite-sample p-value under the assumption that the measured
    phenotype (the dependent variable in the regression) has
    a known Bernoulli-normal mixture distribution.
    Finite-sample genome-wide regression p-values (Gwrpv) with
    a non-normally distributed phenotype (Gregory Connor and
    Michael O'Neill, bioRxiv 204727 <doi:10.1101/204727>).

Fits geographically weighted regression (GWR) models and has tools to diagnose and remediate collinearity in the GWR models. Also fits geographically weighted ridge regression (GWRR) and geographically weighted lasso (GWL) models. See Wheeler (2009) <doi:10.1068/a40256> and Wheeler (2007) <doi:10.1068/a38325> for more details.

Shiny application for the analysis of groundwater
    monitoring data, designed to work with simple time-series data for
    solute concentration and ground water elevation, but can also plot
    non-aqueous phase liquid (NAPL) thickness if required. Also provides
    the import of a site basemap in GIS shapefile format.

The correlations and linkage disequilibrium between tests can vary as a function of minor allele frequency thresholds used to filter variants, and also varies with different choices of test statistic for region-based tests. Appropriate genome-wide significance thresholds can be estimated empirically through permutation on only a small proportion of the whole genome.

A novel PRS model is introduced to enhance the prediction accuracy by utilising GxE effects. This package performs Genome Wide Association Studies (GWAS) and Genome Wide Environment Interaction Studies (GWEIS) using a discovery dataset. The package has the ability to obtain polygenic risk scores (PRSs) for a target sample. Finally it predicts the risk values of each individual in the target sample. Users have the choice of using existing models (Li et al., 2015) <doi:10.1093/annonc/mdu565>, (Pandis et al., 2013) <doi:10.1093/ejo/cjt054>, (Peyrot et al., 2018) <doi:10.1016/j.biopsych.2017.09.009> and (Song et al., 2022) <doi:10.1038/s41467-022-32407-9>, as well as newly proposed models for genomic risk prediction (refer to the URL for more details).