Leverages 'dplyr' to process the calculations of a plot inside a database. 
    This package provides helper functions that abstract the work at three levels:
    outputs a 'ggplot', outputs the calculations, outputs the formula
    needed to calculate bins.

A 'dplyr' back end for databases that allows you to work with
    remote database tables as if they are in-memory data frames.  Basic
    features works with any database that has a 'DBI' back end; more
    advanced features require 'SQL' translation to be provided by the
    package author.

Calculates lower bound on power, upper bound on power, and exact power (small sets only) for detection boundary tests (e.g. Berk-Jones, Generalized Berk-Jones, innovated Berk-Jones) used in set-based inference studies. These detection boundary tests are described in Sun et al., (2019) <doi:10.1080/01621459.2019.1660170>.

Bayesian Beta Regression, adapted for bounded discrete responses, commonly seen in survey responses.
  Estimation is done via Markov Chain Monte Carlo sampling, using a Gibbs wrapper around univariate slice sampler 
  (Neal (2003) <DOI:10.1214/aos/1056562461>), as implemented in the R package MfUSampler 
  (Mahani and Sharabiani (2017) <DOI: 10.18637/jss.v078.c01>).

A fast reimplementation of several density-based algorithms of
    the DBSCAN family. Includes the clustering algorithms DBSCAN (density-based 
    spatial clustering of applications with noise) and HDBSCAN (hierarchical 
    DBSCAN), the ordering algorithm OPTICS (ordering points to identify the 
    clustering structure), shared nearest neighbor clustering, and the outlier 
    detection algorithms LOF (local outlier factor) and GLOSH (global-local 
    outlier score from hierarchies). The implementations use the kd-tree data 
    structure (from library ANN) for faster k-nearest neighbor search. An R 
    interface to fast kNN and fixed-radius NN search is also provided. 
    Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.

Prediction methods where explanatory information is coded as a matrix of distances between individuals. Distances can either be directly input as a distances matrix, a squared distances matrix, an inner-products matrix or computed from observed predictors.