A tool to define the rare biosphere. 'ulrb' solves the problem of the 
    definition of rarity by replacing arbitrary thresholds with an unsupervised machine 
    learning algorithm (partitioning around medoids, or k-medoids). This algorithm 
    works for any type of microbiome data, provided there is an abundance table.
    This method also works for non-microbiome data.

A generic reference Bayesian analysis of unidimensional mixture distributions obtained by a location-scale parameterisation of the model is implemented. The including functions simulate and summarize posterior samples for location-scale mixture models using a weakly informative prior. There is no need to define priors for scale-location parameters except two hyperparameters in which are associated with a Dirichlet prior for weights and a simplex.

Plots traced ultrasound tongue imaging data according to a polar coordinate system. 
    There is currently support for plotting means and standard deviations of each category's trace;
    Smoothing Splines Analysis of Variance (SSANOVA) could be implemented as well.  The origin of the 
    polar coordinates may be defined manually or automatically determined based on different 
    algorithms.
    Currently 'ultrapolaRplot' supports ultrasound tongue imaging trace data from 'UltraTrace' 
    (<https://github.com/SwatPhonLab/UltraTrace>). 'UltraTrace' is capable of importing data from 
    Articulate Instruments AAA.
    'read_textgrid.R' is required for opening TextGrids to determine category and alignment
    information of ultrasound traces.

Uniform manifold approximation and projection is a technique
    for dimension reduction. The algorithm was described by McInnes and
    Healy (2018) in <arXiv:1802.03426>. This package provides an interface
    for two implementations. One is written from scratch, including components
    for nearest-neighbor search and for embedding. The second implementation
    is a wrapper for 'python' package 'umap-learn' (requires separate
    installation, see vignette for more details).