Implementation of various spirometry equations
    in R, currently the GLI-2012 (Global Lung Initiative;
    Quanjer et al. 2012 <doi:10.1183/09031936.00080312>),
    the race-neutral GLI global 2022 (Global Lung Initiative;
    Bowerman et al. 2023 <doi:10.1164/rccm.202205-0963OC>), the
    NHANES3 (National Health and Nutrition Examination Survey;
    Hankinson et al. 1999 <doi:10.1164/ajrccm.159.1.9712108>)
    and the JRS 2014 (Japanese Respiratory Society; Kubota
    et al. 2014 <doi:10.1016/j.resinv.2014.03.003>) equations.
    Also the GLI-2017 diffusing capacity equations 
    <doi:10.1183/13993003.00010-2017> are implemented.
    Contains user-friendly functions to calculate predicted 
    and LLN (Lower Limit of Normal) values for different 
    spirometric parameters such as FEV1 (Forced Expiratory 
    Volume in 1 second), FVC (Forced Vital Capacity), etc, 
    and to convert absolute spirometry measurements 
    to percent (%) predicted and z-scores.

Enables binary package installations on Linux distributions.
    Provides access to 'RStudio' public repositories at
    <https://packagemanager.posit.co>, and transparent management of
    system requirements without administrative privileges. Currently supported
    distributions are 'CentOS' / 'RHEL', and several 'RHEL' derivatives
    ('Rocky Linux', 'AlmaLinux', 'Oracle Linux', and 'Amazon Linux'),
    'openSUSE' / 'SLES', 'Debian', and 'Ubuntu' LTS.

Provides an interface to the Spotify API <https://developer.spotify.com/documentation/web-api/>.

An implementation of functionalities to transform directed graphs that are bound to a set of
  known forbidden paths. There are several transformations, following the rules provided by Villeneuve 
  and Desaulniers (2005) <doi: 10.1016/j.ejor.2004.01.032>, and Hsu et al. (2009) <doi: 10.1007/978-3-642-03095-6_60>. 
  The resulting graph is generated in a data-frame format. See rsppfp website for more information, 
  documentation an examples.

The SPRITE algorithm creates possible distributions of discrete responses
    based on reported sample parameters, such as mean, standard deviation and range 
    (Heathers et al., 2018, <doi:10.7287/peerj.preprints.26968v1>). This package implements it, 
    drawing heavily on the code for Nick Brown's 'rSPRITE' Shiny app <https://shiny.ieis.tue.nl/sprite/>. 
    In addition, it supports the modeling of distributions based on multi-item (Likert-type) 
    scales and the use of restrictions on the frequency of particular responses.