Generates n hierarchical clustering hypotheses on subsets of classifiers (usually species in community ecology studies). The n clustering hypotheses are combined to generate a generalized cluster, and computes three metrics of support. 1) The average proportion of elements conforming the group in each of the n clusters (integrity). And 2) the contamination, i.e., the average proportion of elements from other groups that enter a focal group. 3) The probability of existence of the group gives the integrity and contamination in a Bayesian approach.

A database management tool built as a 'shiny' application. Connect to various 
    databases to send queries, upload files, preview tables, and more.

A simple wrapper for the 'Octopus Energy' API
    <https://developer.octopus.energy/docs/api/>. It handles
    authentication, by storing a provided API key and meter details.
    Implemented endpoints include 'products' for viewing tariff details
    and 'consumption' for viewing meter consumption data.

The aim of 'od' is to provide tools and example datasets for
    working with origin-destination ('OD') datasets of the type used to
    describe aggregate urban mobility patterns (Carey et al. 1981)
    <doi:10.1287/trsc.15.1.32>.  The package builds on functions for
    working with 'OD' data in the package 'stplanr', (Lovelace and Ellison
    2018) <doi:10.32614/RJ-2018-053> with a focus on computational
    efficiency and support for the 'sf' class system (Pebesma 2018)
    <doi:10.32614/RJ-2018-009>.  With few dependencies and a simple class
    system based on data frames, the package is intended to facilitate
    efficient analysis of 'OD' datasets and to provide a place for
    developing new functions.  The package enables the creation and
    analysis of geographic entities representing large scale mobility
    patterns, from daily travel between zones in cities to migration
    between countries.

Pure-R implementation of univariate binary-class ODA (UniODA),
    univariate multiclass ODA (MultiODA), and binary Classification Tree
    Analysis (CTA). Supports ordered and categorical attributes, priors-on
    inverse-frequency weighting, MAXSENS / SAMPLEREP / first-identified
    tie-breaking, true leave-one-out cross-validation, and Monte Carlo
    Fisher-randomization p-values. Covered UniODA, MultiODA, and binary CTA
    fixtures are tested for parity against MegaODA.exe and CTA.exe outputs.