Provides functions to perform the peer performance analysis of
    funds' returns as described in Ardia and Boudt (2018)
    <doi:10.1016/j.jbankfin.2017.10.014>. For each fund, the package estimates
    the proportion of peers it outperforms, is equalled by, and is outperformed
    by, correcting for luck with the false discovery approach of Storey (2002)
    <doi:10.1111/1467-9868.00346>. Screenings can be based on factor-model
    alphas, Sharpe ratios, or modified Sharpe ratios, the latter using the
    equality test of Ardia and Boudt (2015) <doi:10.1016/j.frl.2015.02.008>.
    Funds can be screened within a universe or against a separate peer group,
    over rolling windows, and results come with bootstrap confidence intervals,
    summary, plot, and tidy data frame methods.

Functions for reading, writing, plotting, analysing, and manipulating allelic and haplotypic data, including from VCF files, and for the analysis of population nucleotide sequences and micro-satellites including coalescent analyses, linkage disequilibrium, population structure (Fst, Amova) and equilibrium (HWE), haplotype networks, minimum spanning tree and network, and median-joining networks.

The population proportion using group testing can be estimated by different methods. Four functions including p.mle(), p.gart(), p.burrow() and p.order() are provided to implement four estimating methods including the maximum likelihood estimate, Gart's estimate, Burrow's estimate, and order statistic estimate.

A lightweight, dependency-free, and simplified implementation of the 
    Pseudo-Expectation Gauss-Seidel (PEGS) algorithm. It fits the multivariate 
    ridge regression model for genomic prediction Xavier and Habier (2022) 
    <doi:10.1186/s12711-022-00730-w> and Xavier et al. (2025) 
    <doi:10.1093/genetics/iyae179>, providing heritability estimates, genetic 
    correlations, breeding values, and regression coefficient estimates for 
    prediction. This package provides an alternative to the 'bWGR' package by 
    Xavier et al. (2019) <doi:10.1093/bioinformatics/btz794> by using 'LAPACK'
    for its algebraic operations.