Some utility functions for validation and data manipulation. These functions can be helpful to reduce internal codes everywhere in package development.

Enables launching a series of simulations of a computer code from the R session, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented.

Given the scores from decision makers, the analytic hierarchy process can be conducted easily.

Alluvial plots are similar to sankey diagrams and visualise categorical data 
    over multiple dimensions as flows. (Rosvall M, Bergstrom CT (2010) Mapping Change in 
    Large Networks. PLoS ONE 5(1): e8694. <doi:10.1371/journal.pone.0008694> 
    Their graphical grammar however is a bit more complex then that of a regular x/y 
    plots. The 'ggalluvial' package made a great job of translating that grammar into 
    'ggplot2' syntax and gives you many options to tweak the appearance of an alluvial 
    plot, however there still remains a multi-layered complexity that makes it difficult
    to use 'ggalluvial' for explorative data analysis. 'easyalluvial' provides a simple 
    interface to this package that allows you to produce a decent alluvial plot from any 
    dataframe in either long or wide format from a single line of code while also handling 
    continuous data. It is meant to allow a quick visualisation of entire dataframes 
    with a focus on different colouring options that can make alluvial plots a great 
    tool for data exploration.

Perform analysis of variance and other important complementary
  analyses.  The functions are easy to use.  Performs analysis in various
  designs, with balanced and unbalanced data.

Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. 
    The package supports fitting cross-sectional network models fitted using the packages 'BDgraph', 'bgms' and 'BGGM', 
    as well as network comparison fitted using the 'bgms' and 'BBGM'. 
    The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the 
    posterior density of the parameters. In addition, for 'BDgraph' and 'bgms' it allows to assess the posterior 
    structure space. Furthermore, the package comes with an extensive suite for visualizing results.