c(person("Mateusz", "Dominiak", role = c("aut")), person("Rafal Jankowski", email = "rafal.jankowski@neptune.ai", role = c("aut","cre")))
Mateusz Dominiak [aut],
Rafal Jankowski [aut, cre]

MLOps Metadata Store - Experiment Tracking and Model Registry
for Production Teams
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.
Neptune is used for:
• 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
For more information see <https://neptune.ai/>.

Serves data from the United States Environmental Protection Agency (USEPA) National Eutrophication Survey <https://www.epa.gov/national-aquatic-resource-surveys>.

Mann-Whitney-Wilcoxon Test for Nested Ranks
person(c("Douglas", "G."), "Scofield", email = "douglasgscofield@gmail.com", role = c("aut", "cre"))
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.

Nested Cross-Validation with 'glmnet' and 'caret'
c(person(given = "Myles",family = "Lewis",
role = c("aut", "cre"),
email = "myles.lewis@qmul.ac.uk",
comment = c(ORCID = "0000-0001-9365-5345")),
person(given = "Athina",family = "Spiliopoulou",
role = c("aut"),
comment = c(ORCID = "0000-0002-5929-6585")),
person(given = "Katriona",family = "Goldmann",
role = c("aut"),
email = "k.goldmann@qmul.ac.uk",
comment = c(ORCID = "0000-0002-9073-6323")))
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>.
Boruta, caret, CORElearn, data.table, glmnet, hsstan,
matrixTests, methods, parallel, pROC, randomForest, RcppEigen,
Rfast
Myles Lewis [aut, cre] (<https://orcid.org/0000-0001-9365-5345>),
Athina Spiliopoulou [aut] (<https://orcid.org/0000-0002-5929-6585>),
Katriona Goldmann [aut] (<https://orcid.org/0000-0002-9073-6323>)