Imports: hypergeo, Rdpack, dgof, graphics

Title: Classification with Parallel Factor Analysis
Author: Matthew A. Snodgress <snodg031@umn.edu>
Maintainer: Matthew A. Snodgress <snodg031@umn.edu>
Depends: multiway, glmnet, e1071, randomForest, doParallel
Description: Classification using Richard A. Harshman's Parallel Factors (Parafac) model fit to a 3-way or 4-way data array. Uses Parafac factor weights from one mode of this model as predictors to tune parameters for one or more classification methods. Supports binary and multiclass classification. Predicts class labels or probabilities and calculates multiple classification performance measures. Parallel computing is implemented via the 'parallel' and 'doParallel' packages.
Packaged: 2022-03-07 21:02:12 UTC; msnodgress

Description: Classification using Richard A. Harshman's Parallel Factors (Parafac) model fit to a 3-way or 4-way data array/tensor. See Harshman (1994) <doi:10.1016/0167-9473(94)90132-5>. Uses Parafac factor weights from one mode of this model as predictors to tune parameters for one or more classification methods. Supports penalized logistic regression, support vector machine, random forest, and feed-forward neural network. Supports binary and multiclass classification. Predicts class labels or class probabilities and calculates multiple classification performance measures. Parallel computing is implemented via the 'parallel' and 'doParallel' packages.
Packaged: 2022-04-08 16:02:12 UTC; msnodgress

Description: Classification using Richard A. Harshman's Parallel Factor Analysis (Parafac) model-1 fit to a three-way or four-way data tensor/array. See Harshman (1994) <doi:10.1016/0167-9473(94)90132-5>. Uses Parafac factor weights from one mode of this model as predictors to tune parameters for one or more classification methods via a k-fold cross-validation procedure. Supports penalized logistic regression, support vector machine, random forest, and feed-forward neural network. Supports binary and multiclass classification. Predicts class labels or class probabilities and calculates multiple classification performance measures. Parallel computing is implemented via the 'parallel' and 'doParallel' packages.
Packaged: 2022-05-16 20:12:17 UTC; msnodgress