Three semi-parametric methods for detection of outliers in environmental data based on kernel regression and subsequent analysis of smoothing residuals. The first method (Campulova, Michalek, Mikuska and Bokal (2018) <DOI: 10.1002/cem.2997>) analyzes the residuals using changepoint analysis, the second method is based on control charts (Campulova, Veselik and Michalek (2017) <DOI: 10.1016/j.apr.2017.01.004>) and the third method (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>) analyzes the residuals using extreme value theory (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>).

The purpose of this package is to support the setup the R environment.  
    The two main features are 'autos', to automatically source files and/or 
    directories into your environment, and 'paths' to consistently set path objects 
    across projects for input and output. Both are implemented using a configuration 
    file to allow easy, custom configurations that can be used for multiple 
    or all projects.

Runs a series of configurable tests against a user's compute 
  environment. This can be used for checking that things like a specific 
  directory or an environment variable is available before you start an analysis.
  Alternatively, you can use the package's situation report when filing error
  reports with your compute infrastructure.

Graphical and statistical analyses of environmental data, with 
  focus on analyzing chemical concentrations and physical parameters, usually in 
  the context of mandated environmental monitoring.  Major environmental 
  statistical methods found in the literature and regulatory guidance documents, 
  with extensive help that explains what these methods do, how to use them, 
  and where to find them in the literature.  Numerous built-in data sets from 
  regulatory guidance documents and environmental statistics literature.  Includes 
  scripts reproducing analyses presented in the book "EnvStats:  An R Package for 
  Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, 
  <doi:10.1007/978-1-4614-8456-1>).