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Applies quality control to daily precipitation observations; 
    reconstructs the original series by estimating precipitation in missing values; and 
    creates gridded datasets of daily precipitation.

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.