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.

Computes unidimensional and multidimensional Reciprocity and Inaccuracy indices. These indices are applicable to common heterostylous populations and to any other type of stylar dimorphic and trimorphic populations, such as in enantiostylous and three-dimensional heterostylous plants. Simón-Porcar, V., A. J. Muñoz-Pajares, J. Arroyo, and S. D. Johnson. (in press) "FlowerMate: multidimensional reciprocity and inaccuracy indices for style-polymorphic plant populations."

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.