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.

Functions to create, connect, update and query 'HSQL' databases embedded in Open Document Databases files, as 'OpenOffice' and 'LibreOffice' do.

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>.