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>.

Tools for analysing, comparing, visualising and partitioning diversity based on Hill numbers.
  'hilldiv' is an R package that provides a set of functions to assist analysis of diversity for
  diet reconstruction, microbial community profiling or more general ecosystem characterisation
  analyses based on Hill numbers, using OTU/ASV tables and associated phylogenetic trees as
  inputs. The package includes functions for (phylo)diversity measurement, (phylo)diversity
  profile plotting, (phylo)diversity comparison between samples and groups, (phylo)diversity
  partitioning and (dis)similarity measurement. All of these grounded in abundance-based and
  incidence-based Hill numbers.
  The statistical framework developed around Hill numbers encompasses many of the most
  broadly employed diversity (e.g. richness, Shannon index, Simpson index),
  phylogenetic diversity (e.g. Faith's PD, Allen's H, Rao's quadratic entropy) and
  dissimilarity (e.g. Sorensen index, Unifrac distances) metrics. This enables the most
  common analyses of diversity to be performed while grounded in a single statistical
  framework. The methods are described in Jost et al. (2007) <DOI:10.1890/06-1736.1>,
  Chao et al. (2010) <DOI:10.1098/rstb.2010.0272> and Chiu et al. (2014)
  <DOI:10.1890/12-0960.1>; and reviewed in the framework of molecularly characterised
  biological systems in Alberdi & Gilbert (2019) <DOI:10.1111/1755-0998.13014>.