Optimal experimental designs for both population and individual
    studies based on nonlinear mixed-effect models. Often this is based on a
    computation of the Fisher Information Matrix. This package was developed
    for pharmacometric problems, and examples and predefined models are available
    for these types of systems. The methods are described in Nyberg et al. 
    (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) 
    <doi:10.1016/S0169-2607(03)00073-7>.

Enables computation of epidemiological statistics, including
    those where counts or mortality rates of the reference population are
    used.  Currently supported: excess hazard models (Dickman, Sloggett,
    Hills, and Hakulinen (2012) <doi:10.1002/sim.1597>), rates, mean
    survival times, relative/net survival (in particular the Ederer II
    (Ederer and Heise (1959)) and Pohar Perme (Pohar Perme, Stare, and
    Esteve (2012) <doi:10.1111/j.1541-0420.2011.01640.x>) estimators), and
    standardized incidence and mortality ratios, all of which can be
    easily adjusted for by covariates such as age. Fast splitting and
    aggregation of 'Lexis' objects (from package 'Epi') and other
    computations achieved using 'data.table'.

Estimate commonly used population genomic statistics and generate publication quality figures. 'PopGenHelpR' uses vcf, 'geno' (012), and csv files to generate output.

Provides several data sets and functions to accompany the book "Population Genetics with R: An Introduction for Life Scientists" (2021, ISBN:9780198829546).

Provides beginner friendly framework to analyse population genetic
    data. Based on 'adegenet' objects it uses 'knitr' to create comprehensive reports on spatial genetic data. 
    For detailed information how to use the package refer to the comprehensive
    tutorials or visit <http://www.popgenreport.org/>.

Implementation of assumption-lean and data-adaptive post-prediction inference (POPInf), for valid and efficient statistical inference based on data predicted by machine learning. See Miao, Miao, Wu, Zhao, and Lu (2023) <arXiv:2311.14220>.