Standard and extensible Eddy-Covariance data post-processing 
  (Wutzler et al. (2018) <doi:10.5194/bg-15-5015-2018>)
  includes  
  uStar-filtering, gap-filling, and flux-partitioning.
  The Eddy-Covariance (EC)  micrometeorological technique quantifies continuous 
  exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere.
  It is important for understanding ecosystem dynamics and upscaling exchange fluxes.
  (Aubinet et al. (2012) <doi:10.1007/978-94-007-2351-1>).
  This package inputs pre-processed (half-)hourly data and supports further processing. 
  First, a quality-check and filtering is performed based on the relationship between 
  measured flux and friction
  velocity (uStar) to discard biased data 
  (Papale et al. (2006) <doi:10.5194/bg-3-571-2006>).
  Second, gaps in the data are filled based on information from environmental conditions
  (Reichstein et al. (2005) <doi:10.1111/j.1365-2486.2005.001002.x>).
  Third, the net flux of carbon dioxide is partitioned
  into its gross fluxes in and out of the ecosystem by night-time 
  based and day-time based approaches
  (Lasslop et al. (2010) <doi:10.1111/j.1365-2486.2009.02041.x>).

Extension to 'REddyProc' that allows reading data from netCDF files.

Implementation of the Robust Exponential Decreasing Index (REDI),
    proposed in the article by Issa Moussa, Arthur Leroy et al. (2019)
    <https://bmjopensem.bmj.com/content/bmjosem/5/1/e000573.full.pdf>.
    The REDI represents a measure of cumulated workload, robust to missing data,
    providing control of the decreasing influence of workload over time. 
    Various functions are provided to format data, compute REDI, and 
    visualise results in a simple and convenient way.

Biologically relevant, yet mathematically sound constraints are used
    to compute the propensity and thence infer the dominant direction of reactions
    of a generic biochemical network. The reactions must be unique and their 
    number must exceed that of the reactants,i.e., reactions >= reactants + 2.  
            'ReDirection', computes the null space of a user-defined stoichiometry
    matrix. The spanning non-zero and unique reaction vectors (RVs) are 
    combinatorially summed to generate one or more subspaces recursively.  
            Every reaction is represented as a sequence of identical components 
    across all RVs of a particular subspace. The terms are evaluated with 
    (biologically relevant bounds, linear maps, tests of convergence, descriptive
    statistics, vector norms) and the terms are classified into forward-, reverse- 
    and equivalent-subsets. Since, these are mutually exclusive the probability 
    of occurrence is binary (all, 1; none, 0).
            The combined propensity of a reaction is the p1-norm of the 
    sub-propensities, i.e., sum of the products of the probability and maximum
    numeric value of a subset (least upper bound, greatest lower bound). This, 
    if strictly positive is the probable rate constant, is used to infer dominant
    direction and annotate a reaction as "Forward (f)", "Reverse (b)" or 
    "Equivalent (e)".
            The inherent computational complexity (NP-hard) per iteration suggests
    that a suitable value for the number of reactions is around 20.
            Three functions comprise ReDirection. These are check_matrix() and 
    reaction_vector() which are internal, and calculate_reaction_vector()
            which is external.