Log-ratio Lasso regression for continuous, binary, and survival outcomes with compositional features. See Fei and others (2023) <doi:10.1101/2023.05.02.538599>.

Visualize as flow diagrams the logic of functions, expressions or 
  scripts in a static way or when running a call, visualize the dependencies between
  functions or between modules in a shiny app, and more.

Creates participant flow diagrams directly from a dataframe. Representing the flow of participants through each stage of a study, especially in clinical trials, is essential to assess the generalisability and validity of the results. This package provides a set of functions that can be combined with a pipe operator to create all kinds of flowcharts from a data frame in an easy way.

Provides functionality to perform machine-learning-based modeling in a computation pipeline.
    Its functions contain the basic steps of machine-learning-based knowledge discovery workflows,
    including model training and optimization, model evaluation, and model testing.
    To perform these tasks, the package builds heavily on existing machine-learning packages,
    such as 'caret' <https://github.com/topepo/caret/> and associated packages.
    The package can train multiple models, optimize model hyperparameters by performing a grid search
    or a random search, and evaluates model performance by different metrics.
    Models can be validated either on a test data set, or in case of a small sample size
    by k-fold cross validation or repeated bootstrapping.
    It also allows for 0-Hypotheses generation by performing permutation experiments.
    Additionally, it offers methods of model interpretation and item categorization
    to identify the most informative features from a high dimensional data space.
    The functions of this package can easily be integrated into computation pipelines
    (e.g. 'nextflow' <https://www.nextflow.io/>) and hereby improve scalability,
    standardization, and re-producibility in the context of machine-learning.