Component-wise gradient boosting for analysis of multiply
    imputed datasets. Implements the algorithm Boosting after Multiple
    Imputation (MIBoost), which enforces uniform variable selection across
    imputations and provides utilities for pooling. Includes a cross-validation
    workflow that first splits the data into training and validation sets and
    then performs imputation on the training data, applying the learned
    imputation models to the validation data to avoid information leakage.
    Supports Gaussian and logistic loss. Methods relate to gradient boosting
    and multiple imputation as in Buehlmann and Hothorn (2007) <doi:10.1214/07-STS242>,
    Friedman (2001) <doi:10.1214/aos/1013203451>, and van Buuren (2018, ISBN:9781138588318)
    and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; see also Kuchen (2025)
    <doi:10.48550/arXiv.2507.21807>.

Make some distributions from the 'C++' library 'Boost'
    available in 'R'. In addition, the normal-inverse Gaussian distribution
    and the generalized inverse Gaussian distribution are provided. The 
    distributions are represented by 'R6' classes. The method to sample from 
    the generalized inverse Gaussian distribution is the one given in 
    "Random variate generation for the generalized inverse Gaussian 
    distribution" Luc Devroye (2012) <doi:10.1007/s11222-012-9367-z>.

Output formats and utilities for authoring books and technical documents with R Markdown.

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) when variables are categorical, Multiple Factor Analysis (MFA) when variables are structured in groups.

Tools for optimal and approximate state estimation as well as
    network inference of Partially-Observed Boolean Dynamical Systems.

Functions to reconstruct, generate, and simulate synchronous, asynchronous, probabilistic, and temporal Boolean networks. Provides also functions to analyze and visualize attractors in Boolean networks <doi:10.1093/bioinformatics/btq124>.