Package for a Network assisted algorithm for Epigenetic studies using mean and variance Combined signals: NEpiC. NEpiC combines both signals in mean and variance differences in methylation level between case and control groups searching for differentially methylated sub-networks (modules) using the protein-protein interaction network.

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/>.

Clinical reporting figures require to use consistent colors
    and configurations. As a part of the Roche open-source clinical
    reporting project, namely the NEST project, the 'nestcolor' package
    specifies the color code and default theme with specifying 'ggplot2'
    theme parameters. Users can easily customize color and theme settings
    before using the reset of NEST packages to ensure consistent settings
    in both static and interactive output at the downstream.

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 <doi:10.1093/bioadv/vbad048>. 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>. Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) <doi:10.1214/17-EJS1337SI>.