An implementation of the ALFAM2 dynamic emission model for ammonia volatilization from field-applied animal slurry (manure with dry matter below about 15%). The model can be used to predict cumulative emission and emission rate of ammonia following field application of slurry. Predictions may be useful for emission inventory calculations, fertilizer management, assessment of mitigation strategies, or research aimed at understanding ammonia emission. Default parameter sets include effects of application method, slurry composition, and weather. The model structure is based on a simplified representation of the physical-chemical slurry-soil-atmosphere system. More information is available via citation("ALFAM2").

Allows you to connect to an 'Alfresco' content management repository and interact
  with its contents using simple and intuitive functions.  You will be able to establish a connection session to the 'Alfresco' repository,
  read and upload content and manage folder hierarchies.  For more details on the 'Alfresco' content management repository
  see <https://www.alfresco.com/ecm-software/document-management>.

Provides direct access to the ALFRED (<https://alfred.stlouisfed.org>) and FRED (<https://fred.stlouisfed.org>) databases.
    Its functions return tidy data frames for different releases of the specified time series. 
    Note that this product uses the FRED© API but is not endorsed or certified by the Federal Reserve Bank of St. Louis.

Algorithmic experimental designs. Calculates exact and
        approximate theory experimental designs for D,A, and I
        criteria. Very large designs may be created. Experimental
        designs may be blocked or blocked designs created from a
        candidate list, using several criteria.  The blocking can be
        done when whole and within plot factors interact.

Provides an algebra over probability distributions enabling
    composition, sampling, and automatic simplification to closed forms.
    Supports normal, exponential, gamma, Weibull, chi-squared, uniform,
    beta, log-normal, Poisson, multivariate normal, empirical, and mixture
    distributions with algebraic operators (addition, subtraction,
    multiplication, division, power, exp, log, min, max) that automatically
    simplify when mathematical identities apply. Includes closed-form MVN
    conditioning (Schur complement), affine transformations, mixture
    marginals/conditionals (Bayes rule), and limiting distribution builders
    (CLT, LLN, delta method). Uses S3 classes for distributions and R6
    for support objects.