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.

Allows to get weather data from Automated Surface Observing
    System (ASOS) stations (airports) in the whole world thanks to the
    Iowa Environment Mesonet website.

We provide a variety of algorithms for manifold-valued data, including Fréchet summaries, hypothesis testing, clustering, visualization, and other learning tasks. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> for general exposition to statistics on manifolds.

We provide a number of algorithms to estimate fundamental statistics including Fréchet mean and geometric median for manifold-valued data. Also, C++ header files are contained that implement elementary operations on manifolds such as Sphere, Grassmann, and others. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> if you are interested in statistics on manifolds, and Absil et al (2007, ISBN:9780691132983) on computational aspects of optimization on matrix manifolds.

Implements various Riemannian metrics for symmetric positive definite matrices, including AIRM (Affine Invariant Riemannian Metric, see Pennec, Fillard, and Ayache (2006) <doi:10.1007/s11263-005-3222-z>), Log-Euclidean (see Arsigny, Fillard, Pennec, and Ayache (2006) <doi:10.1002/mrm.20965>), Euclidean, Log-Cholesky (see Lin (2019) <doi:10.1137/18M1221084>), and Bures-Wasserstein metrics (see Bhatia, Jain, and Lim (2019) <doi:10.1016/j.exmath.2018.01.002>). Provides functions for computing logarithmic and exponential maps, vectorization, and statistical operations on the manifold of positive definite matrices.