The algorithm assigns rareness/ outlierness score to every sample in voluminous datasets.
    The algorithm makes multiple estimations of the proximity between a pair of samples, in low-dimensional spaces. To compute proximity, FiRE uses Sketching, a variant of locality sensitive hashing. For more details: Jindal, A., Gupta, P., Jayadeva and Sengupta, D., 2018. Discovery of rare cells from voluminous single cell expression data. Nature Communications, 9(1), p.4719. <doi:10.1038/s41467-018-07234-6>.

Authenticate users in 'Shiny' applications using 'Google Firebase'
    with any of the many methods provided; email and password, email link, or
    using a third-party provider such as 'Github', 'Twitter', or 'Google'.
    Use 'Firebase Storage' to store files securely, and leverage 'Firebase Analytics'
    to easily log events and better understand your audience.

A convenient and user-friendly interface to 
    interact with the 'Firebase Authentication REST API': <https://firebase.google.com/docs/reference/rest/auth>.
    It enables R developers to integrate 'Firebase Authentication' services 
    seamlessly into their projects, allowing for user authentication, account 
    management, and other authentication-related tasks.

Fire behavior prediction models, including the Scott & Reinhardt's (2001) Rothermel Wildland Fire Modelling System <DOI:10.2737/RMRS-RP-29> and Alexander et al.'s (2006) Crown Fire Initiation & Spread model <DOI:10.1016/j.foreco.2006.08.174>. Also contains sample datasets, estimation of fire behavior prediction model inputs (e.g., fuel moisture, canopy characteristics, wind adjustment factor), results visualization, and methods to estimate fire weather hazard.

Methods for computing and visualizing wildfire ignition exposure 
    and directional vulnerability that are published in a series of scientific 
    publications are automated by the functions in this package. See Beverly 
    et al. (2010) <doi:10.1071/WF09071>, Beverly et al. (2021) 
    <doi:10.1007/s10980-020-01173-8>, and Beverly and Forbes (2023) 
    <doi:10.1007/s11069-023-05885-3> for background and methodology.