The stepwise regression with assumptions checking and the possible Box-Cox transformation.

Fit Bayesian stochastic block models (SBMs) and multi-level stochastic block models (MLSBMs) using efficient Gibbs sampling implemented in 'Rcpp'. The models assume symmetric, non-reflexive graphs (no self-loops) with unweighted, binary edges. Data are input as a symmetric binary adjacency matrix (SBMs), or list of such matrices (MLSBMs).

Generate a stream of pseudo-random numbers generated using the MLS 
    Junk Generator algorithm. Functions exist to generate single pseudo-random 
    numbers as well as a vector, data frame, or matrix of pseudo-random numbers.

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>.