Biologically relevant, yet mathematically sound constraints are used
    to compute the propensity and thence infer the dominant direction of reactions
    of a generic biochemical network. The reactions must be unique and their 
    number must exceed that of the reactants,i.e., reactions >= reactants + 2.  
            'ReDirection', computes the null space of a user-defined stoichiometry
    matrix. The spanning non-zero and unique reaction vectors (RVs) are 
    combinatorially summed to generate one or more subspaces recursively.  
            Every reaction is represented as a sequence of identical components 
    across all RVs of a particular subspace. The terms are evaluated with 
    (biologically relevant bounds, linear maps, tests of convergence, descriptive
    statistics, vector norms) and the terms are classified into forward-, reverse- 
    and equivalent-subsets. Since, these are mutually exclusive the probability 
    of occurrence is binary (all, 1; none, 0).
            The combined propensity of a reaction is the p1-norm of the 
    sub-propensities, i.e., sum of the products of the probability and maximum
    numeric value of a subset (least upper bound, greatest lower bound). This, 
    if strictly positive is the probable rate constant, is used to infer dominant
    direction and annotate a reaction as "Forward (f)", "Reverse (b)" or 
    "Equivalent (e)".
            The inherent computational complexity (NP-hard) per iteration suggests
    that a suitable value for the number of reactions is around 20.
            Three functions comprise ReDirection. These are check_matrix() and 
    reaction_vector() which are internal, and calculate_reaction_vector()
            which is external.

Providing the container for the DockerParallel package.

An optimized method for identifying mutually 
    exclusive genomic events.  Its main contribution is a
    statistical analysis based on the Poisson-Binomial 
    distribution that takes into account that some samples
    are more mutated than others. See [Canisius, Sander, John WM Martens,
    and  Lodewyk FA Wessels. (2016)  "A novel independence test for
    somatic alterations in cancer shows that 
    biology drives mutual exclusivity but chance explains
    most co-occurrence." Genome biology 17.1 : 1-17. <doi:10.1186/s13059-016-1114-x>].
    The mutations matrices are sparse matrices. The method developed takes 
    advantage of the advantages of this type of matrix to save 
    time and computing resources.