Various utilities to manipulate multivariate polynomials

Various utilities to manipulate multivariate polynomials.

Calculate Multivariate Richness via UTC and sUTC
Functions to calculate Unique Trait Combinations (UTC) and
scaled Unique Trait Combinations (sUTC) as measures of multivariate
richness. The package can also calculate beta-diversity for trait richness and
can partition this into nestedness-related and turnover components. The
code will also calculate several measures of overlap.

Functions to calculate Unique Trait Combinations (UTC) and scaled
Unique Trait Combinations (sUTC) as measures of multivariate richness. The
package can also calculate beta-diversity for trait richness and can
partition this into nestedness-related and turnover components. The code
will also calculate several measures of overlap.

Functions to calculate Unique Trait Combinations (UTC) and scaled
Unique Trait Combinations (sUTC) as measures of multivariate richness. The
package can also calculate beta-diversity for trait richness and can
partition this into nestedness-related and turnover components. The code
will also calculate several measures of overlap. See Keyel and Wiegand
(2016) <doi:10.1111/2041-210X.12558> for more details.

Fast Cross-Validation for Multi-Penalty Ridge Regression
Mark A. van de Wiel <mark.vdwiel@amsterdamumc.nl>
R (>= 3.5.0), survival, pROC, methods, mgcv, snowfall
Multi-penalty linear, logistic and cox ridge regression, including estimation of the penalty parameters by efficient (repeated) cross-validation and marginal likelihood maximization. Multiple high-dimensional data types that require penalization are allowed, as well as unpenalized variables. Paired and preferential data types can be specified. See Van de Wiel et al. (2021), <arXiv:2005.09301>.

R (>= 3.5.0), survival, pROC, risksetROC, mgcv, snowfall

Selecting Combinations of Predictors by Leveraging Multiple AUCs
for an Ordered Multilevel Outcome
Uses multiple AUCs to select a combination of predictors when the outcome has multiple (ordered) levels and the focus is discriminating one particular level from the others. This method is most naturally applied to settings where the outcome has three levels. (Meisner, A, Parikh, CR, and Kerr, KF (2017) <http://biostats.bepress.com/uwbiostat/paper423/>.)