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.

Simultaneous clustering of rows and columns, usually designated by
    biclustering, co-clustering or block clustering, is an important technique
    in two way data analysis. It consists of estimating a mixture model which
    takes into account the block clustering problem on both the individual and
    variables sets. The 'blockcluster' package provides a bridge between the C++
    core library build on top of the 'STK++' library, and the R statistical
    computing environment. This package allows to co-cluster binary
    <doi:10.1016/j.csda.2007.09.007>, contingency <doi:10.1080/03610920903140197>,
    continuous <doi:10.1007/s11634-013-0161-3> and categorical data-sets
    <doi:10.1007/s11222-014-9472-2>. It also provides utility functions to
    visualize the results. This package may be useful for various applications
    in fields of Data mining, Information retrieval, Biology, computer vision
    and many more. More information about the project and comprehensive tutorial
    can be found on the link mentioned in URL.

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>.

Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.