Title: MLOps Metadata Store - Experiment Tracking and Model Registry
Description: An interface to Neptune. A metadata store for MLOps, built for teams that run a lot of experiments.
It gives you a single place to log, store, display, organize, compare, and query all your model-building metadata.
• Experiment tracking: Log, display, organize, and compare ML experiments in a single place.
• Model registry: Version, store, manage, and query trained models, and model building metadata.
• Monitoring ML runs live: Record and monitor model training, evaluation, or production runs live

Title: National Eutrophication Survey Data
Description: Serves data from the United States Environmental Protection Agency (USEPA) National Eutrophication Survey <https://www.epa.gov/national-aquatic-resource-surveys>.
Imports: rappdirs, dplyr, purrr, readr, dataone

Title: Mann-Whitney-Wilcoxon Test for Nested Ranks
Authors@R: person(c("Douglas", "G."), "Scofield", email = "douglasgscofield@gmail.com", role = c("aut", "cre"))
Maintainer: Douglas G. Scofield <douglasgscofield@gmail.com>
Description: Calculate a Mann-Whitney-Wilcoxon test for a difference between treatment levels using nested ranks.  This test can be used when observations are structured into several groups and each group has received both treatment levels. The p-value is determined via bootstrapping.  The nested ranks test is intended to be one possible mixed-model extension of the Mann-Whitney-Wilcoxon test, for which treatment is a fixed effect and group membership is a random effect.
Packaged: 2015-02-08 14:23:49 UTC; douglasgscofield

Packaged: 2015-06-06 11:58:54 UTC; douglasgscofield

Title: Nested Cross-Validation with 'glmnet' and 'caret'
person(given = "Athina",family = "Spiliopoulou",
Description: Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) <doi:10.1111/j.2517-6161.1977.tb01603.x>.
Imports: Boruta, caret, CORElearn, data.table, glmnet, hsstan,
matrixTests, methods, parallel, pROC, randomForest, RcppEigen,