A high-level plotting system, compatible with `ggplot2` objects, 
    maps from `sf`, `terra`, `raster`, `sp`. It is built primarily on the 
    'grid' package. The objective of the package is to provide a plotting system 
    that is built for speed and modularity. This is useful for quick visualizations 
    when testing code and for plotting multiple figures to the same device from 
    independent sources that may be independent of one another (i.e., different 
    function or modules the create the visualizations).  The suggested package 
    'fastshp' can be installed from the repository 
    (<https://PredictiveEcology.r-universe.dev>).

Quickly fits and plots psychometric functions (normal, logistic,
    Weibull or any or any function defined by the user) for multiple groups.

A collection of functions for constructing large pairwised comparisons 
             and rating them using Elo rating system with supporting parallel processing.
             The method of random sample pairs is based on Reservoir Sampling proposed by 
             JVitter (1985) <doi:10.1145/3147.3165>.

A set of functions to extract results from regression models and
 plot the effect size using 'ggplot2' seamlessly. While 'broom' is useful to
 convert statistical analysis objects into tidy data frames, 'coefplot' is adept at showing
 multivariate regression results. With specific outcome, this package could build regression models
 automatically, extract results into a data frame and provide a quicker way to summarize
 models' statistical findings using 'ggplot2'.

Helps to perform linear regression analysis by reducing manual effort. Reduces the independent variables based on specified p-value and Variance Inflation Factor (VIF) level.

Test whether equality and order constraints hold for all 
    individuals simultaneously by comparing Bayesian mixed models through Bayes 
    factors. A tutorial style vignette and a quickstart guide are available, via
    vignette("manual", "quid"), and vignette("quickstart", "quid") respectively.
    See Haaf and Rouder (2017) <doi:10.1037/met0000156>; Haaf, Klaassen and Rouder
    (2019) <doi:10.31234/osf.io/a4xu9>; and Rouder & Haaf (2021) <doi:10.5334/joc.131>.