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>.

Wrapper around various existing tools and command-line interfaces,
  providing a standard interface, simple parallelization, and detailed logging.
  For microarray data, maps probe sets to standard gene IDs, building on
  'GEOquery' Davis and Meltzer (2007) <doi:10.1093/bioinformatics/btm254>,
  'ArrayExpress' Kauffmann et al. (2009) <doi:10.1093/bioinformatics/btp354>,
  Robust multi-array average 'RMA' Irizarry et al. (2003) <doi:10.1093/biostatistics/4.2.249>,
  and 'BrainArray' Dai et al. (2005) <doi:10.1093/nar/gni179>.
  For RNA-seq data, fetches metadata and raw reads from National Center for Biotechnology
  Information (NCBI) Sequence Read Archive (SRA), performs standard adapter and
  quality trimming using 'TrimGalore' Krueger <https://github.com/FelixKrueger/TrimGalore>,
  performs quality control checks using 'FastQC' Andrews <https://github.com/s-andrews/FastQC>,
  quantifies transcript abundances using 'salmon' Patro et al. (2017) <doi:10.1038/nmeth.4197> and potentially
  'refgenie' Stolarczyk et al. (2020) <doi:10.1093/gigascience/giz149>,
  aggregates the results using 'MultiQC' Ewels et al. (2016) <doi:10.1093/bioinformatics/btw354>,
  maps transcripts to genes using 'biomaRt' Durinkck et al. (2009) <doi:10.1038/nprot.2009.97>,
  and summarizes transcript-level quantifications for gene-level analyses using
  'tximport' Soneson et al. (2015) <doi:10.12688/f1000research.7563.2>.