Cross-validate one or multiple regression and classification models
    and get relevant evaluation metrics in a tidy format. Validate the
    best model on a test set and compare it to a baseline evaluation.
    Alternatively, evaluate predictions from an external model. Currently
    supports regression and classification (binary and multiclass).
    Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., 
    & Wambugu M. (2019, ISBN: 9781838550134).

Perform canonical variate regression (CVR) for two sets of covariates and a univariate
            response, with regularization and weight parameters tuned by cross validation.

Calculate various cardiovascular disease risk scores from the
    Framingham Heart Study (FHS), the American College of Cardiology (ACC),
    and the American Heart Association (AHA) as described in D’agostino, et al
    (2008) <doi:10.1161/circulationaha.107.699579>, Goff, et al (2013)
    <doi:10.1161/01.cir.0000437741.48606.98>, and Mclelland, et al (2015)
    <doi:10.1016/j.jacc.2015.08.035>.

The goal of 'cvsem' is to provide functions that allow for comparing Structural Equation Models (SEM) using cross-validation. Users can specify multiple SEMs using 'lavaan' syntax. 'cvsem' computes the Kullback Leibler (KL) Divergence between 1) the model implied covariance matrix estimated from the training data and 2) the sample covariance matrix estimated from the test data described in Cudeck, Robert & Browne (1983) <doi:10.18637/jss.v048.i02>. The KL Divergence is computed for each of the specified SEMs allowing for the models to be compared based on their prediction errors.

The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating under-performing candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.