Analysis of seed germination data
    using the physiological time modelling approach. Includes functions
    to fit hydrotime and thermal-time models with the traditional approaches
    of Bradford (1990) <doi:10.1104/pp.94.2.840>
    and Garcia-Huidobro (1982) <doi:10.1093/jxb/33.2.288>. 
    Allows to fit models to grouped datasets,
    i.e. datasets containing multiple species, seedlots or experiments.

Regression analysis using common models in seed temperature studies, such as the Gaussian model (Martins, JF, Barroso, AAM, & Alves, PLCA (2017) <doi:10.1590/s0100-83582017350100039>), quadratic (Nunes, AL, Sossmeier, S, Gotz, AP, & Bispo, NB (2018) <doi: 10.17265/2161-6264/2018.06.002>) and others with potential for use, such as those implemented in the 'drc' package (Ritz, C, Baty, F, Streibig, JC, & Gerhard, D (2015). <doi:10.1371/journal.pone.0146021>), in the estimation of the ideal and cardinal temperature for the occurrence of plant seed germination. The functions return graphs with the equations automatically.

Algorithms to calculate the hidden inputs of systems of differential equations. 
  These hidden inputs can be interpreted as a control that tries to minimize the
  discrepancies between a given model and taken measurements. The idea is 
  also called the Dynamic Elastic Net, as proposed in the paper "Learning (from) the errors of a systems biology model" 
  (Engelhardt, Froelich, Kschischo 2016) <doi:10.1038/srep20772>.
  To use the experimental SBML import function, the 'rsbml' package is required. For installation I refer to the official 'rsbml' page: <https://bioconductor.org/packages/release/bioc/html/rsbml.html>.

Seed vigor is defined as the sum total of those properties of the seed which determine the level of activity and performance of the seed or seed lot during germination and seedling emergence.  Testing for vigor becomes more important for carryover seeds, especially if seeds were stored under unknown conditions or under unfavorable storage conditions. Seed vigor testing is also used as indicator of the storage potential of a seed lot and in ranking various seed lots with different qualities. The vigour index is calculated using the equation given by (Ling et al. 2014) <doi:10.1038/srep05859>.