Provides an R wrapper to the 'CVD Prevent' application
    programming interface (API). Users can make API requests through
    built-in R functions. The Cardiovascular Disease Prevention Audit 
    (CVDPREVENT) is an England-wide primary care audit that automatically 
    extracts routinely held GP health data. 
    <https://bmchealthdocs.atlassian.net/wiki/spaces/CP/pages/317882369/CVDPREVENT+API+Documentation>.

Contains functions for testing for significant differences between multiple coefficients of variation. Includes Feltz and Miller's (1996) <DOI:10.1002/(SICI)1097-0258(19960330)15:6%3C647::AID-SIM184%3E3.0.CO;2-P> asymptotic test and Krishnamoorthy and Lee's (2014) <DOI:10.1007/s00180-013-0445-2> modified signed-likelihood ratio test. See the vignette for more, including full details of citations.

Calculates predictions from generalized estimating equations and internally cross-validates them using the logarithmic, quadratic and spherical proper scoring rules; Kung-Yee Liang and Scott L. Zeger (1986) <doi:10.1093/biomet/73.1.13>.

Estimates a lasso penalized precision matrix via the blockwise coordinate descent (BCD). This package is a simple wrapper around the popular 'glasso' package that extends and enhances its capabilities. These enhancements include built-in cross validation and visualizations.
              See Friedman et al (2008) <doi:10.1093/biostatistics/kxm045> for details regarding the estimation method.

Efficient implementations of cross-validation techniques for linear and ridge regression models, leveraging 'C++' code with 'Rcpp', 'RcppParallel', and 'Eigen' libraries. It supports leave-one-out, generalized, and K-fold cross-validation methods, utilizing 'Eigen' matrices for high performance. Methodology references: Hastie, Tibshirani, and Friedman (2009) <doi:10.1007/978-0-387-84858-7>.

Produce an averaging estimate/prediction by combining all candidate models for partial linear functional additive models, using multi-fold cross-validation criterion. More details can be referred to arXiv e-Prints via <doi:10.48550/arXiv.2105.00966>.