A collection of 'HTML', 'JavaScript', and 'CSS' assets that
  dynamically generate beautiful documentation from a 'Swagger' compliant API:
  <https://swagger.io/specification/>.

Provides a fast implementation of the SWAG algorithm for Generalized Linear Models which allows to perform a meta-learning procedure that combines 
    screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. The package then performs test on the network of selected models to identify the variables that are highly predictive by using entropy-based network measures.

Collection of functions to connect the structure of the data with the information on the samples. Three types of associations are covered: 1. linear model of principal components. 2. hierarchical clustering analysis. 3. distribution of features-sample annotation associations. Additionally, the inter-relation between sample annotations can be analyzed. Simple methods are provided for the correction of batch effects and removal of principal components.

Obtaining accurate and stable estimates of regression coefficients
    can be challenging when the suggested statistical model has issues related to
    multicollinearity, convergence, or overfitting. One solution is to use
    principal component analysis (PCA) results in the regression, as discussed in
    Chan and Park (2005) <doi:10.1080/01446190500039812>. The swaprinc() package
    streamlines comparisons between a raw regression model with the full set of
    raw independent variables and a principal component regression model where
    principal components are estimated on a subset of the independent variables,
    then swapped into the regression model in place of those variables. The
    swaprinc() function compares one raw regression model to one principal
    component regression model, while the compswap() function compares one raw
    regression model to many principal component regression models. Package
    functions include parameters to center, scale, and undo centering and scaling,
    as described by Harvey and Hansen (2022)
    <https://cran.r-project.org/package=LearnPCA/vignettes/Vig_03_Step_By_Step_PCA.pdf>.
    Additionally, the package supports using Gifi methods to extract principal
    components from categorical variables, as outlined by Rossiter (2021)
    <https://www.css.cornell.edu/faculty/dgr2/_static/files/R_html/NonlinearPCA.html#2_Package>.