A collection of fast and flexible functions for analyzing
    omics data in observational studies. Multiple different approaches for
    integrating multiple environmental/genetic factors, omics data, and/or
    phenotype data are implemented. This includes functions for performing
    omics wide association studies with one or more variables of interest
    as the exposure or outcome; a function for performing a meet in the
    middle analysis for linking exposures, omics, and outcomes (as
    described by Chadeau-Hyam et al., (2010)
    <doi:10.3109/1354750X.2010.533285>); and a function for performing a
    mixtures analysis across all omics features using quantile-based
    g-Computation (as described by Keil et al., (2019)
    <doi:10.1289/EHP5838>).

A toolbox to make it easy to analyze plant disease epidemics. It
    provides a common framework for plant disease intensity data recorded over
    time and/or space. Implemented statistical methods are currently mainly
    focused on spatial pattern analysis (e.g., aggregation indices, Taylor and
    binary power laws, distribution fitting, SADIE and 'mapcomp' methods). See
    Laurence V. Madden, Gareth Hughes, Franck van den Bosch (2007)
    <doi:10.1094/9780890545058> for further information on these methods.
    Several data sets that were mainly published in plant disease epidemiology
    literature are also included in this package.

Tools for the analysis of epidemiological and surveillance data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, computation of confidence intervals around incidence risk and incidence rate estimates and sample size calculations for cross-sectional, case-control and cohort studies. Surveillance tools include functions to calculate an appropriate sample size for 1- and 2-stage representative freedom surveys, functions to estimate surveillance system sensitivity and functions to support scenario tree modelling analyses.