Environmental health studies are increasingly measuring multiple pollutants
    to characterize the joint health effects attributable to exposure mixtures.
    However, the underlying dose-response relationship between toxicants and
    health outcomes of interest may be highly nonlinear, with possible nonlinear
    interaction effects. Hierarchical integrative group least absolute shrinkage
    and selection operator (HiGLASSO), developed by Boss et al (2020)
    <arXiv:2003.12844>, is a general framework to identify noteworthy nonlinear 
    main and interaction effects in the presence of group structures among a set
    of exposures.

Implements the Hierarchical Incremental GRAdient Descent (HiGrad) algorithm,
    a first-order algorithm for finding the minimizer of a function in online learning just like stochastic gradient descent (SGD).
    In addition, this method attaches a confidence interval to assess the uncertainty of its predictions.
    See Su and Zhu (2018) <arXiv:1802.04876> for details.

Provides utilities for encoding and decoding coordinates to/from Hilbert curves
  based on the iterative encoding implementation described in Chen et al. (2006) <doi:10.1002/spe.793>.

Quantifying similarity between high-dimensional single cell samples is challenging, and usually requires
    some simplifying hypothesis to be made. By transforming the high dimensional space into a high dimensional grid,
    the number of cells in each sub-space of the grid is characteristic of a given sample. Using a Hilbert curve
    each sample can be visualized as a simple density plot, and the distance between samples can be calculated from
    the distribution of cells using the Jensen-Shannon distance. Bins that correspond to significant differences
    between samples can identified using a simple bootstrap procedure.

Makes it easy to extract and combine variables from the HILDA (Household, Income and Labour Dynamics in Australia) survey maintained by the Melbourne Institute <https://melbourneinstitute.unimelb.edu.au/hilda>.