Graphical visualization of the birds' molt to facilitate the creation of molting graph for passerines having 9 (Rmolt(data,9)) or 10 primaries (Rmolt(data,10)), and also only for the 10 first primaries (Rmolt(data,"10_0")).

The 'R' equivalent of 'nodemon'. Watches specified
    directories for file changes and reruns a designated 'R' script when
    changes are detected. It's designed to automate the process of
    reloading your 'R' applications during development, similar to
    'nodemon' for 'Node.js'.

Integrated tools to support rigorous and well documented data 
    harmonization based on Maelstrom Research guidelines. The package includes 
    functions to assess and prepare input elements, apply specified processing 
    rules to generate harmonized datasets, validate data processing and identify 
    processing errors, and document and summarize harmonized outputs. The 
    harmonization process is defined and structured by two key user-generated 
    documents: the DataSchema (specifying the list of harmonized variables to 
    generate across datasets) and the Data Processing Elements (specifying the 
    input elements and processing algorithms to generate harmonized variables 
    in DataSchema formats). The package was developed to address key  challenges 
    of retrospective data harmonization in epidemiology (as described in 
    Fortier I and al. (2017) <doi:10.1093/ije/dyw075>) but can be used for any 
    data harmonization initiative.

Encrypt R objects to a raw vector or file using modern cryptographic
    techniques.  Password-based key derivation is with 'Argon2' (<https://en.wikipedia.org/wiki/Argon2>).
    Objects are serialized and then encrypted using 'XChaCha20-Poly1305' (<https://en.wikipedia.org/wiki/ChaCha20-Poly1305>)
    which follows RFC 8439 for authenticated encryption (<https://en.wikipedia.org/wiki/Authenticated_encryption>).
    Cryptographic functions are provided by the included 'monocypher' 'C' library (<https://monocypher.org>).