Linear and logistic ridge regression functions. Additionally includes special functions for 
            genome-wide single-nucleotide polymorphism (SNP) data. More details can be found in
            <doi: 10.1002/gepi.21750> and <doi: 10.1186/1471-2105-12-372>.

Implements an extension of Bayesian Additive Regression Trees (BART) 
    in which each regression tree outputs a linear combination of random ridge functions 
    (i.e., a composition of a non-linear function like cosine, hyperbolic tangent, the rectified linear 
    unit with an affine transformation) instead of a constant. 
    Can be used to perform "targeted smoothing" in which trees split on certain covariates 
    but output smooth functions in other covariates. For more information, see
    Yee, Ghosh, and Deshpande (2026+) <doi:10.48550/arXiv.2411.07984>.

Implementation of a Principal Component Analysis (PCA) in the
    torus via density ridge estimation. The main function, ridge_pca(), obtains
    the relevant density ridge for bivariate sine von Mises and bivariate
    wrapped Cauchy distribution models and provides the associated scores and
    variance decomposition. Auxiliary functions for evaluating, fitting, and
    sampling these models are also provided. The package provides replicability
    to García-Portugués and Prieto-Tirado (2023)
    <doi:10.1007/s11222-023-10273-9>.

It is a package that provides alternative approach for finding optimum parameters of ridge regression. This package focuses on finding the ridge parameter value k which makes the variance inflation factors closest to 1, while keeping them above 1 as addressed by Michael Kutner, Christopher Nachtsheim, John Neter, William Li (2004, ISBN:978-0073108742). Moreover, the package offers end-to-end functionality to find optimum k value and presents the detailed ridge regression results. Finally it shows three sets of graphs consisting k versus variance inflation factors, regression coefficients and standard errors of them.

An interface to iDigBio's search API that allows downloading
  specimen records. Searches are returned as a data.frame. Other functions
  such as the metadata end points return lists of information. iDigBio is a US
  project focused on digitizing and serving museum specimen collections on the
  web. See <https://www.idigbio.org> for information on iDigBio.