A toolkit for making antigenic maps from immunological assay data,
    in order to quantify and visualize antigenic differences between different
    pathogen strains as described in
    Smith et al. (2004) <doi:10.1126/science.1097211> and used in the World
    Health Organization influenza vaccine strain selection process. Additional
    functions allow for the diagnostic evaluation of antigenic maps and an
    interactive viewer is provided to explore antigenic relationships amongst
    several strains and incorporate the visualization of associated genetic
    information.

A methodology to perform multivariate measurement error adjustment using external validation data. Allows users to remove the attenuating effect of measurement error by incorporating a distribution of external validation data, and allows for plotting of all resultant adjustments. Sensitivity analyses can also be run through this package to test how different ranges of validity coefficients can impact the effect of the measurement error adjustment. The methods implemented in this package are based on the work by Muoka, A., Agogo, G., Ngesa, O., Mwambi, H. (2020): <doi:10.12688/f1000research.27892.1>.

Interface for loading data from 'ActiveCampaign API v3' 
    <https://developers.activecampaign.com/reference>. Provide functions
    for getting data by deals, contacts, accounts, campaigns and messages.

Systematically transform immunoassay data, evaluate if the data is normally distributed, and pick the right method for cut point determination based on that evaluation. This package can also produce plots that are needed for reports, so data analysis and visualization can be done easily.

Implementation of the MaxRank normalization method, which enables 
     standardization of Rank Abundance Distributions (RADs) to a specified 
     number of ranks. Rank abundance distributions are widely used in biology 
     and ecology to describe species abundances, and are mathematically 
     equivalent to complementary cumulative distribution functions (CCDFs) used 
     in physics, linguistics, sociology, and other fields. The method is 
     described in Saeedghalati et al. (2017) <doi:10.1371/journal.pcbi.1005362>.