Graphical methods for compactly illustrating probability distributions, including density strips, density regions, sectioned density plots and varying width strips, using base R graphics.   Note that the 'ggdist' package offers a similar set of tools for illustrating distributions, based on 'ggplot2'.

The 'dentomedical' package provides a comprehensive suite of tools for
 medical and dental research. It includes automated descriptive statistics,
 bivariate analysis with intelligent test selection, logistic regression,
 and diagnostic accuracy assessment. All functions generate publication-ready tables using 'flextable', ensuring reproducibility and
 clarity suitable for manuscripts, reports, and clinical research workflows.

Provides the mathematical model described by "Serostatus Testing & Dengue Vaccine Cost-Benefit Thresholds"
    in <doi:10.1098/rsif.2019.0234>.  Using the functions in the package,
    that analysis can be repeated using sample life histories, either synthesized from local seroprevalence data
    using other functions in this package (as in the manuscript) or from some other source.
    The package provides a vignette which walks through the analysis in the publication, as well as a function
    to generate a project skeleton for such an analysis.

Implements the Differential Evolution algorithm for global
    optimization of a real-valued function of a real-valued parameter
    vector as described in Mullen et al. (2011) <doi:10.18637/jss.v040.i06>.

Differential Evolution (DE) stochastic heuristic algorithms for
    global optimization of problems with and without general constraints.
    The aim is to curate a collection of its variants that
    (1) do not sacrifice simplicity of design,
    (2) are essentially tuning-free, and
    (3) can be efficiently implemented directly in the R language.
    Currently, it provides implementations of the algorithms 'jDE' by
    Brest et al. (2006) <doi:10.1109/TEVC.2006.872133> for single-objective
    optimization and 'NCDE' by Qu et al. (2012) <doi:10.1109/TEVC.2011.2161873>
    for multimodal optimization (single-objective problems with
    multiple solutions).