Functions to support compatibility between 'Maelstrom' R packages 
     and 'Opal' environment. 'Opal' is the 'OBiBa' core database application for 
     biobanks. It is used to build data repositories that integrates data 
     collected from multiple sources. 'Opal Maelstrom' is a specific 
     implementation of this software. This 'Opal' client is specifically 
     designed to interact with 'Opal Maelstrom' distributions to perform 
     operations on the R server side. The user must have adequate credentials.
     Please see <https://opaldoc.obiba.org/> for complete documentation.

Enhances 'mlexperiments'
    <https://CRAN.R-project.org/package=mlexperiments> with additional
    machine learning ('ML') learners for survival analysis. The package
    provides R6-based survival learners for the following algorithms:
    'glmnet' <https://CRAN.R-project.org/package=glmnet>, 'ranger'
    <https://CRAN.R-project.org/package=ranger>, 'xgboost'
    <https://CRAN.R-project.org/package=xgboost>, and 'rpart'
    <https://CRAN.R-project.org/package=rpart>. These can be used directly
    with the 'mlexperiments' R package.

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. A package vignette is available from <DOI:10.32614/CRAN.package.mlt.docreg> and
  more convenient user interfaces to many models from <DOI:10.32614/CRAN.package.tram>.

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.