Performs analysis of regression in simple designs with quantitative treatments, 
             including mixed models and non linear models.

The main function, ProtectTable(), performs table suppression according to a 
 frequency rule with a data set as the only required input. Within this function, 
 protectTable(), protect_linked_tables() or runArgusBatchFile() in package 'sdcTable' is called. 
 Lists of level-hierarchy (parameter 'dimList') and other required input to these functions 
 are created automatically. 
 The suppression method Gauss (default) is implemented independently of 'sdcTable'.
 The function, PTgui(), starts a graphical user interface based on the 'shiny' package.

A meta-package that installs and loads a set of packages from 
    'easystats' ecosystem in a single step. This collection of packages provide 
    a unifying and consistent framework for statistical modeling, visualization, 
    and reporting. Additionally, it provides articles targeted at instructors for 
    teaching 'easystats', and a dashboard targeted at new R users for easily 
    conducting statistical analysis by accessing summary results, model fit indices, 
    and visualizations with minimal programming.

This SVG elements generator can easily generate 
    SVG elements such as rect, line, circle, ellipse, polygon, 
    polyline, text and group. Also, it can combine and 
    output SVG elements into a SVG file.

Set of tools to simplify application of atomic forecast
    verification metrics for (comparative) verification of ensemble forecasts
    to large data sets. The forecast metrics are imported from the
    'SpecsVerification' package, and additional forecast metrics are provided
    with this package. Alternatively, new user-defined forecast scores can be
    implemented using the example scores provided and applied using the
    functionality of this package.

Functions are provided to determine production frontiers and technical 
    efficiency measures through non-parametric techniques based upon regression trees. 
    The package includes code for estimating radial input, output, directional and 
    additive measures, plotting graphical representations of the scores and the production 
    frontiers by means of trees, and determining rankings of importance of input variables 
    in the analysis. Additionally, an adaptation of Random Forest by a set of individual 
    Efficiency Analysis Trees for estimating technical efficiency is also included. More 
    details in: <doi:10.1016/j.eswa.2020.113783>.