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>.  Shift-scale (Siegfried et al, 2023, <DOI:10.1080/00031305.2023.2203177>)
  and multivariate (Klein et al, 2022, <DOI:10.1111/sjos.12501>) transformation models
  are part of this package.

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.

Fit multilevel manifest or latent time-series models, including popular Dynamic Structural Equation Models (DSEM).
  The models can be set up and modified with user-friendly functions and are fit to the data using 'Stan' for Bayesian inference.
  Path models and formulas for user-defined models can be easily created with functions using 'knitr'. 
  Asparouhov, Hamaker, & Muthen (2018) <doi:10.1080/10705511.2017.1406803>.

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.