Data for the extraterrestrial solar spectral irradiance and ground 
    level solar spectral irradiance and irradiance. In addition data for 
    shade light under vegetation and irradiance time series from different
    broadband sensors.  Part of the 
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

Constructors of waveband objects for commonly used biological
    spectral weighting functions (BSWFs) and for different wavebands describing
    named ranges of wavelengths in the ultraviolet (UV), visible (VIS)
    and infrared (IR) regions of the electromagnetic spectrum.  Part of the
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

Read, process, fit, and analyze photosynthetic gas exchange
    measurements. Documentation is provided by several vignettes; also see
    Lochocki, Salesse-Smith, & McGrath (2025) <doi:10.1111/pce.15501>.

Features unstructured, structured and reverse geocoding using the
    'photon' geocoding API <https://photon.komoot.io/>.
    Facilitates the setup of local 'photon' instances to enable offline
    geocoding.

Connect R to the PhotosynQ platform (<https://photosynq.org>). It allows to login and logout,
    as well as receive project information and project data. Further it transforms the received JSON objects
    into a data frame, which can be used for the final data analysis.

Provides functions for modeling, comparing, and visualizing photosynthetic light response curves using established mechanistic and empirical models like the rectangular hyperbola Michaelis-Menton based models ((eq1 (Baly (1935) <doi:10.1098/rspb.1935.0026>)) (eq2 (Kaipiainenn (2009) <doi:10.1134/S1021443709040025>)) (eq3 (Smith (1936) <doi:10.1073/pnas.22.8.504>))), hyperbolic tangent based models ((eq4 (Jassby & Platt (1976) <doi:10.4319/LO.1976.21.4.0540>)) (eq5 (Abe et al. (2009) <doi:10.1111/j.1444-2906.2008.01619.x>))), the non-rectangular hyperbola model (eq6 (Prioul & Chartier (1977) <doi:10.1093/oxfordjournals.aob.a085354>)), exponential based models ((eq8 (Webb et al. (1974) <doi:10.1007/BF00345747>)), (eq9 (Prado & de Moraes (1997) <doi:10.1007/BF02982542>))), and finally the Ye model (eq11 (Ye (2007) <doi:10.1007/s11099-007-0110-5>)). Each of these nonlinear least squares models are commonly used to express photosynthetic response under changing light conditions and has been well supported in the literature, but distinctions in each mathematical model represent moderately different assumptions about physiology and trait relationships which ultimately produce different calculated functional trait values. These models were all thoughtfully discussed and curated by Lobo et al. (2013) <doi:10.1007/s11099-013-0045-y> to express the importance of selecting an appropriate model for analysis, and methods were established in Davis et al. (in review) to evaluate the impact of analytical choice in phylogenetic analysis of the function-valued traits. Gas exchange data on 28 wild sunflower species from Davis et al.are included as an example data set here.