Maintainer: Eric F. Lock <elock@umn.edu>
Description: Functions to predict one multi-way array (i.e., a tensor) from another multi-way array, using a low-rank CANDECOMP/PARAFAC (CP) factorization and a ridge (L_2) penalty [Lock, EF (2018) <doi:10.1080/10618600.2017.1401544>].  Also includes functions to sample from the Bayesian posterior of a tensor-on-tensor model.

Title: Variable Importance Measures for Multivariate Random Forests
c(person("Sikdar", "Sharmistha", email = "sharmistha.sikdar@tuck.dartmouth.edu", role = "aut"),
person("Hooker", "Giles", email = "ghooker@berkeley.edu", role="aut"),
person("Kadiyali","Vrinda", email = "kadiayali@cornell.edu", role="ctb"),
person("Dogonadze","Nika", email = "nika.dogonadze@toptal.com", role="cre"))
Description: Calculates post-hoc variable importance measures for multivariate random forests.
These are given by split improvement for splits defined by feature j as measured using user-defined (i.e. training or test) examples.
Importance measures can also be calculated on a per-outcome variable basis using the change in predictions for each split. Both measures can be
optionally thresholded to include only splits that produce statistically significant changes as measured by an F-test.
Imports: MultivariateRandomForest (>= 1.1.5), MASS (>= 7.3.0)

Description: Calculates two sets of post-hoc variable importance measures for multivariate random forests. The first set of variable importance measures are given by the sum of mean split improvements for splits defined by feature j measured on user-defined examples (i.e., training or testing samples). The second set of importance measures are calculated on a per-outcome variable basis as the sum of mean absolute difference of node values for each split defined by feature j measured on user-defined examples (i.e., training or testing samples). The user can optionally threshold both sets of importance measures to include only splits that are statistically significant as measured using an F-test.