Provides R bindings for Google's s2 library for geometric calculations on
    the sphere. High-performance constructors and exporters provide high compatibility
    with existing spatial packages, transformers construct new geometries from existing
    geometries, predicates provide a means to select geometries based on spatial
    relationships, and accessors extract information about geometries.

A set of functions used in teaching STATS 201/208 Data Analysis at
    the University of Auckland. The functions are designed to make parts of R more
    accessible to a large undergraduate population who are mostly not statistics
    majors.

An advanced version of package 's2dverification'. Intended for 
    seasonal to decadal (s2d) climate forecast verification, but also applicable
    to other types of forecasts or general climate analysis. This package is 
    specifically designed for comparing experimental and observational datasets. 
    It provides functionality for data retrieval, post-processing, skill score 
    computation against observations, and visualization. Compared to 
    's2dverification', 's2dv' is more compatible with the package 'startR', able 
    to use multiple cores for computation and handle multi-dimensional arrays 
    with a higher flexibility. The Climate Data Operators (CDO) version used in 
    development is 1.9.8. Implements methods described in Wilks (2011) 
    <doi:10.1016/B978-0-12-385022-5.00008-7>, DelSole and Tippett 
    (2016) <doi:10.1175/MWR-D-15-0218.1>, Kharin et al. (2012) 
    <doi:10.1029/2012GL052647>, Doblas-Reyes et al. (2003) 
    <doi:10.1007/s00382-003-0350-4>.

Implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models.  We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in 'C++' using 'RcppArmadillo' and integrated into R via 'Rcpp' modules. See Culp, M. 2013 <doi:10.1080/10618600.2012.657139> for references on the Joint Trained Elastic-Net.