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.

Make querying on 'OData' easier. It exposes an 'ODataQuery' object
    that can be manipulated and provides features such as selection, filtering
    and ordering.

A DBI-compatible interface to ODBC databases.

Download data from Brazil's Origin Destination Surveys. The package covers both data from household travel surveys, dictionaries of variables, and the spatial geometries of surveys conducted in different years and across various urban areas in Brazil. For some cities, the package will include enhanced versions of the data sets with variables "harmonized" across different years.

Anomaly detection in dynamic, temporal networks. The package 
    'oddnet' uses a feature-based method to identify anomalies. First, it computes 
    many features for each network. Then it models the features using time series 
    methods. Using time series residuals it detects anomalies. This way, the 
    temporal dependencies are accounted for when identifying anomalies 
    (Kandanaarachchi, Hyndman 2022) <arXiv:2210.07407>.