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'.

Lipid Maps Rest service. Researchers can access the Lipid Maps Rest
  service programmatically and conveniently integrate it into the current workflow
  or packages.

Lipid annotation in untargeted LC-MS lipidomics based on fragmentation rules. Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A (2019) <doi:10.1021/acs.analchem.8b03409>.

Create lipidome-wide heatmaps of statistics with the 'lipidomeR'.
    The 'lipidomeR' provides a streamlined pipeline for the systematic 
    interpretation of the lipidome through publication-ready visualizations 
    of regression models fitted on lipidomics data. 
    With 'lipidomeR', associations between covariates and the lipidome 
    can be interpreted systematically and intuitively through heatmaps, where 
    lipids are categorized by the lipid class and are presented on 
    two-dimensional maps organized by the lipid size and level of saturation. 
    This way, the 'lipidomeR' helps you gain an immediate understanding of 
    the multivariate patterns in the lipidome already at first glance.
    You can create lipidome-wide heatmaps of statistical associations, changes, 
    differences, variation, or other lipid-specific values.  
    The heatmaps are provided with publication-ready quality and the results 
    behind the visualizations are based on rigorous statistical models.