Likelihood-based estimation of conditional transformation
  models via the most likely transformation approach described in
  Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020)
  <DOI:10.18637/jss.v092.i01>.

Additional documentation, a package vignette and 
  regression tests for package mlt.

A fast, robust and easy-to-use calculation of multi-class classification evaluation metrics based on confusion matrix.

A collection of machine learning helper functions, particularly assisting in the Exploratory Data Analysis phase. Makes heavy use of the 'data.table' package for optimal speed and memory efficiency. Highlights include a versatile bin_data() function, sparsify() for converting a data.table to sparse matrix format with one-hot encoding, fast evaluation metrics, and empirical_cdf() for calculating empirical Multivariate Cumulative Distribution Functions.

Estimates the multi-level vector autoregression model on time-series data.
             Three network structures are obtained: temporal networks, contemporaneous
             networks and between-subjects networks.

Simulation, inference and clustering of multilevel networks using a
    Stochastic Block Model framework as described in Chabert-Liddell, Barbillon,
    Donnet and Lazega (2021) <doi:10.1016/j.csda.2021.107179>. 
    A multilevel network is defined as the junction of two interaction networks, 
    the upper level or inter-organizational level and the lower level or 
    inter-individual level. The inter-level represents an affiliation 
    relationship.

Simulation and visualization of complex
    models for longitudinal data. The models are encoded using the model coding
    language 'Mlxtran' and automatically converted into C++ codes. 
    That allows one to implement very easily complex ODE-based models and complex 
    statistical models, including mixed effects models, for continuous, count, 
    categorical, and time-to-event data.

Estimation functions and diagnostic tools for mean length-based total mortality estimators based on Gedamke and Hoenig (2006) <doi:10.1577/T05-153.1>.