dbmovMF: Diagonal Block Mixture of von Mises-Fisher Dsitributions
In dbmovMFs/DirecCoclus: Directional Co-clustering

Provides a simple function to bluid and fit a block mixture of von Mises-Fisher for data co-clustering.

1	dbmovMF(X, k, control = list(), ...)

`X`	A sparse or dense data matrix, where rows denote objets lying on a unit-hypersphere and column denote features. Supported format include Matrix, dgCMatrix, dgTMatrix, etc.
`k`	The number of mixture compoenents (or clusters).
`control`	A list if additional parameters, see Details.
`...`	A list of additional parameters (overriding those specified in control).

In addition to the parameters described above, the function dbmovMF supports other useful parameters including:

max_iter: the maximum number of iteration
max_st_iter: the maximum number of stochatic iterations. Only useful when performing learning using either SAEMb or CAEMb
n_init: number of time the algorithm will be run with different initializations. The final results correspond to the bust run in terms of log likelihood.
tol: tolerance relatve to the log likelihood to declar convergence.
equal_prop: if true then then all mixture proportion will be consider to be equal, and the mixture propotions parmameters alpha will be ignored.
fit_algo: learning algorithm to use. Supported algorithms include: EMb, CEMb, SEMb, SAEMb and CAEMb.
equal_kappa: if true then all concentration parameters kappa of the differrent mixture components will be equal
kappa_: The value of the concentration parameter. This parameter is only effective when euqal_kappa = TRUE.
row_init: a partition of rows into k cluster to be used for initialization. Could be a vector if n_init = 1 of a matrix of size (n_init x n_rows) if n_init>1. Default = NULL.
col_init: same as row_init but for columns.