Provides a comprehensive framework for linear regression modeling and associated statistical analysis. The package implements methods for correlation analysis, including computation of correlation matrices with corresponding significance levels and visualization via correlation heatmaps. It supports estimation of multiple linear regression models, along with automated model selection through backward elimination procedures based on statistical significance criteria. In addition, the package offers a suite of diagnostic tools to assess key assumptions of linear regression, including multicollinearity using variance inflation factors, heteroscedasticity using the Goldfeld-Quandt test, and normality of residuals using the Shapiro-Wilk test. These functionalities, as described in Draper and Smith (1998) <doi:10.1002/9781118625590>, are designed to facilitate robust model building, evaluation, and interpretation in applied statistical and data analytical contexts.

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.