Functions to convert between weather metrics, including conversions
    for metrics of temperature, air moisture, wind speed, and precipitation.
    This package also includes functions to calculate the heat index from
    air temperature and air moisture.

Provides automated downloading, parsing and formatting of
    weather data for Australia through API endpoints provided by the
    Department of Primary Industries and Regional Development (DPIRD) of
    Western Australia and by the Science and Technology Division of the
    Queensland Government's Department of Environment and Science (DES).
    As well as the Bureau of Meteorology (BOM) of the Australian
    government precis and coastal forecasts,
    and downloading and importing radar and satellite imagery files.
    DPIRD weather data are accessed through public APIs provided by
    DPIRD, <https://www.dpird.wa.gov.au/online-tools/apis/>, providing
    access to weather station data from the DPIRD weather station
    network.  Australia-wide weather data are based on data from the
    Australian Bureau of Meteorology (BOM) data and accessed through
    SILO (Scientific Information for Land Owners) Jeffrey et al. (2001)
    <doi:10.1016/S1364-8152(01)00008-1>.  DPIRD data are made available
    under a Creative Commons Attribution 3.0 Licence (CC BY 3.0 AU)
    license <https://creativecommons.org/licenses/by/3.0/au/deed.en>.
    SILO data are released under a Creative Commons Attribution 4.0
    International licence (CC BY 4.0)
    <https://creativecommons.org/licenses/by/4.0/>. BOM data are (c)
    Australian Government Bureau of Meteorology and released under a
    Creative Commons (CC) Attribution 3.0 licence or Public Access Licence
    (PAL) as appropriate, see
    <https://www.bom.gov.au/copyright> for further details.

A comprehensive suite of functions for processing,
  analyzing, and visualizing textual data from tweets is offered.  
  Users can clean tweets, analyze their sentiments, visualize data, 
  and examine the correlation between sentiments and environmental 
  data such as weather conditions. Main features include text processing,  
  sentiment analysis, data visualization, correlation analysis, and 
  synthetic data generation. Text processing involves cleaning and preparing  
  tweets by removing textual noise and irrelevant words. Sentiment analysis 
  extracts and accurately analyzes sentiments from tweet texts using advanced 
  algorithms. Data visualization creates various charts like word clouds
  and sentiment polarity graphs for visual representation of data. Correlation 
  analysis examines and calculates the correlation between tweet sentiments 
  and environmental variables such as weather conditions. Additionally, 
  random tweets can be generated for testing and evaluating the performance 
  of analyses, empowering users to effectively analyze and interpret 'Twitter' 
  data for research and commercial purposes.