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

Plug-in and difference-based long-run covariance matrix estimation for time series regression. Two applications of hypothesis testing are also provided. The first one is for testing for structural stability in coefficient functions. The second one is aimed at detecting long memory in time series regression. Lujia Bai and Weichi Wu (2024)<doi:10.3150/23-BEJ1680> Zhou Zhou and Wei Biao Wu(2010)<doi:10.1111/j.1467-9868.2010.00743.x> Jianqing Fan and Wenyang Zhang<doi:10.1214/aos/1017939139> Lujia Bai and Weichi Wu(2024)<doi:10.1093/biomet/asae013> Dimitris N. Politis, Joseph P. Romano, Michael Wolf(1999)<doi:10.1007/978-1-4612-1554-7> Weichi Wu and Zhou Zhou(2018)<doi:10.1214/17-AOS1582>.

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.