Interactive visualization of effects, response functions 
    and marginal effects for different kinds of regression models. In this version 
    linear regression models, generalized linear models, generalized additive
    models and linear mixed-effects models are supported.  
    Major features are the interactive approach and the handling of the effects of categorical covariates: 
    if two or more factors are used as covariates every combination of the levels of each 
    factor is treated separately. The automatic calculation of 
    marginal effects and a number of possibilities to customize the graphical output 
    are useful features as well.

Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators,
  following the method developed by Y. Baraud, C. Giraud and S. Huet (2014) <doi:10.1214/13-AIHP539>.
  In particular it solves the problem of variable selection by choosing the best predictor among predictors emanating from different methods as lasso,
  elastic-net, adaptive lasso, pls, randomForest. Moreover, it can be applied for choosing the tuning parameter in a Gauss-lasso procedure.

Variable elimination (Gaussian elimination, Fourier-Motzkin elimination), 
    Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution,  
    projecting a vector on the convex polytope described by a system of (in)equations, 
    simplify systems by removing spurious columns and rows and collapse implied equalities, 
    test if a matrix is totally unimodular, compute variable ranges implied by linear
    (in)equalities.

Checks adherence to a given style, syntax errors and possible
    semantic issues.  Supports on the fly checking of R code edited with
    'RStudio IDE', 'Emacs', 'Vim', 'Sublime Text', 'Atom' and 'Visual
    Studio Code'.

Extends the functionality of the 'tourr' package by an interactive
             graphical user interface. The interactivity allows users to
             effortlessly refine their 'tourr' results by manual intervention,
             which allows for integration of expert knowledge and aids the
             interpretation of results. For more information on 'tourr' see
             Wickham et. al (2011) <doi:10.18637/jss.v040.i02> or
             <https://github.com/ggobi/tourr>.