Depends: multiway, glmnet, e1071, randomForest, nnet

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 and Lundy (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.

Title: Parallelized Genomic Prediction and GWAS
Description: Frequently used methods in genomic applications with emphasis on parallel computing (OpenMP).
At its core, the package has a Gibbs Sampler that allows running univariate linear
mixed models that have both, sparse and dense design matrices. The parallel sampling method
in case of dense design matrices (e.g. Genotypes) allows running Ridge Regression or BayesA for
a very large number of individuals. The Gibbs Sampler is capable of running Single Step Genomic Prediction models.
In addition, the package offers parallelized functions for common tasks like genome-wide
association studies and cross validation in a memory efficient way.

Description: A general test for conditional independence in supervised learning
algorithms as proposed by Watson & Wright (2021) <doi:10.1007/s10994-021-06030-6>.
Implements a conditional variable importance measure which can be applied to any supervised
learning algorithm and loss function. Provides statistical inference procedures without
parametric assumptions and applies equally well to continuous and categorical predictors
Suggests: BEST, mlr3learners, ranger, glmnet, testthat (>= 3.0.0),
Author: Marvin N. Wright [aut, cre] (<https://orcid.org/0000-0002-8542-6291>),