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>.

Anscombe's quartet are a set of four two-variable datasets that 
    have several common summary statistics but which have very different joint 
    distributions.  This becomes apparent when the data are plotted, which 
    illustrates the importance of using graphical displays in Statistics.  This
    package enables the creation of datasets that have identical marginal sample
    means and sample variances, sample correlation, least squares regression 
    coefficients and coefficient of determination.  The user supplies an initial 
    dataset, which is shifted, scaled and rotated in order to achieve target 
    summary statistics.  The general shape of the initial dataset is retained. 
    The target statistics can be supplied directly or calculated based on a 
    user-supplied dataset.  The 'datasauRus' package 
    <https://cran.r-project.org/package=datasauRus> provides further examples 
    of datasets that have markedly different scatter plots but share many 
    sample summary statistics.

Functions and data to accompany the 5th edition of the book "Applied Nonparametric Statistical Methods" (4th edition: Sprent & Smeeton, 2024, ISBN:158488701X), the revisions from the 4th edition including a move from describing the output from a miscellany of statistical software packages to using R. While the output from many of the functions can also be obtained using a range of other R functions, this package provides functions in a unified setting and give output using both p-values and confidence intervals, exemplifying the book's approach of treating p-values as a guide to statistical importance and not an end product in their own right.