A chemical speciation and toxicity prediction model for the 
    toxicity of metals to aquatic organisms. The Biotic Ligand Model (BLM) 
    engine was originally programmed in 'PowerBasic' by Robert Santore and 
    others. The main way the BLM can be used is to predict the toxicity of a 
    metal to an organism with a known sensitivity (i.e., it is known how much 
    of that metal must accumulate on that organism's biotic ligand to cause a 
    physiological effect in a certain percentage of the population, such as a 
    20% loss in reproduction or a 50% mortality rate). The second way the BLM 
    can be used is to estimate the chemical speciation of the metal and other 
    constituents in water, including estimating the amount of metal accumulated 
    to an organism's biotic ligand during a toxicity test. In the first 
    application of the BLM, the amount of metal associated with a toxicity 
    endpoint, or regulatory limit will be predicted, while in the 
    second application, the amount of metal is known and the portions of that 
    metal that exist in various forms will be determined. This version of the 
    engine has been re-structured to perform the calculations in a different 
    way that will make it more efficient in R, while also making it more 
    flexible and easier to maintain in the future. Because of this, it does 
    not currently match the desktop model exactly, but we hope to improve 
    this comparability in the future.

Posterior distribution in the Black-Litterman model is computed from a prior distribution given in the form of a time series of asset returns and a continuous distribution of views provided by the user as an external function.

R's raw vector is useful for storing a single binary object.
    What if you want to put a vector of them in a data frame? The 'blob'
    package provides the blob object, a list of raw vectors, suitable for
    use as a column in data frame.

Computation of large covariance matrices having a block structure up to a permutation of their columns and rows 
    from a small number of samples with respect to the dimension of the matrix.
 The method is described in the paper Perrot-Dockès et al. (2019) <arXiv:1806.10093>.