Tracks elapsed clock time using a `hms::hms()` scalar. 
 It was was originally developed to time Bayesian model runs. 
 It should not be used to estimate how long extremely fast code takes to execute 
 as the package code adds a small time cost.

The heterogeneous multi-task feature learning is a data integration method to conduct joint feature selection across multiple related data sets with different distributions. The algorithm can combine different types of learning tasks, including linear regression, Huber regression, adaptive Huber, and logistic regression. The modified version of Bayesian Information Criterion (BIC) is produced to measure the model performance. Package is based on Yuan Zhong, Wei Xu, and Xin Gao (2022) <https://www.fields.utoronto.ca/talk-media/1/53/65/slides.pdf>.

Hierarchical and single-level non-negative matrix factorization. Several NMF algorithms are available.

Generates (half-)normal plots with simulation envelopes using different diagnostics from a range of different fitted models. A few example datasets are included.

The Hierarchical Neyman-Pearson (H-NP) classification framework
    extends the Neyman-Pearson classification paradigm to multi-class settings
    where classes have a natural priority ordering. This is particularly useful
    for classification in unbalanced dataset, for example, disease severity
    classification, where under-classification errors (misclassifying patients
    into less severe categories) are more consequential than other
    misclassifications. The package implements H-NP umbrella algorithms that
    controls under-classification errors under user specified control levels
    with high probability. It supports the creation of H-NP classifiers using
    scoring functions based on built-in classification methods (including
    logistic regression, support vector machines, and random forests), as well
    as user-trained scoring functions. The package exports `base_function()`
    to train these built-in base learners directly for use in the H-NP
    pipeline.