Analysis of moderation (ANOMO) method conceptualizes
    the difference and equivalence tests as a moderation problem to 
    test the difference and equivalence of 
    two estimates (e.g., two means or two effects).

Analyses of Proportions can be performed on the Anscombe (arcsine-related) transformed  
    data. The 'ANOPA' package can analyze proportions obtained from up to 
    four factors. The factors can be within-subject or between-subject or a mix 
    of within- and between-subject. The main, omnibus analysis can be followed by
    additive decompositions into interaction effects, main effects, simple 
    effects, contrast effects, etc., mimicking precisely the logic of ANOVA. For
    that reason, we call this set of tools 'ANOPA' (Analysis of Proportion
    using Anscombe transform) to highlight its similarities with ANOVA. 
    The 'ANOPA' framework also allows plots of proportions easy to obtain 
    along with confidence intervals. Finally, effect sizes and planning statistical 
    power are easily done under this framework. Only particularity, the 'ANOPA' computes  
    F statistics which have an infinite degree of freedom on the denominator. 
    See Laurencelle and Cousineau (2023) <doi:10.3389/fpsyg.2022.1045436>.

An interactive document on  the topic of one-way and two-way analysis of variance using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at <https://tinyurl.com/ANOVAStatsTool>.

An interactive document on  the topic of one-way and two-way analysis of variance using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/ANOVAShiny/>.

Epidemiological population dynamics models traditionally define 
    a pathogen's virulence as the increase in the per capita rate of mortality 
    of infected hosts due to infection. This package provides functions 
    allowing virulence to be estimated by maximum likelihood techniques. The 
    approach is based on the analysis of relative survival comparing survival 
    in matching cohorts of infected vs. uninfected hosts (Agnew 2019) 
    <doi:10.1101/530709>.