It provides functions and examples for maximum likelihood estimation for
              generalized linear mixed models and Gibbs sampler for multivariate linear
              mixed models with incomplete data, as described in Schafer JL (1997)
              "Imputation of missing covariates under a multivariate linear mixed model".
              Technical report 97-04, Dept. of Statistics, The Pennsylvania State University.

Identification of the most appropriate pharmacotherapy for each 
    patient based on genomic alterations is a major challenge in personalized 
    oncology. 'PANACEA' is a collection of personalized anti-cancer drug 
    prioritization approaches utilizing network methods. The methods utilize 
    personalized "driverness" scores from 'driveR' to rank drugs, mapping these
    onto a protein-protein interaction network. The "distance-based" method 
    scores each drug based on these scores and distances between drugs and genes
    to rank given drugs. The "RWR" method propagates these scores via a 
    random-walk with restart framework to rank the drugs. The methods are 
    described in detail in Ulgen E, Ozisik O, Sezerman OU. 2023. PANACEA: 
    network-based methods for pharmacotherapy prioritization in personalized 
    oncology. Bioinformatics <doi:10.1093/bioinformatics/btad022>.

Provides the tools needed to benchmark the R2 value corresponding to a certain acceptable noise level while also providing a rescaling function based on that noise level yielding a new value of R2 we refer to as R2k which is independent of both the number of degrees of freedom and the noise distribution function.

Provides function for performing Bayesian survival regression using 
    Horseshoe prior in the accelerated failure time model with log normal assumption 
    in order to achieve high dimensional pan-cancer variable selection as developed in
    Maity et. al. (2019) <doi:10.1111/biom.13132>.

Contains some functions catching all messages, 'stdout' and other
    useful information while evaluating R code and other helpers to return user
    specified text elements (like: header, paragraph, table, image, lists etc.)
    in 'pandoc' markdown or several type of R objects similarly automatically
    transformed to markdown format. Also capable of exporting/converting (the
    resulting) complex 'pandoc' documents to e.g. HTML, 'PDF', 'docx' or 'odt'. This
    latter reporting feature is supported in brew syntax or with a custom reference
    class with a smarty caching 'backend'.