fmlcdEM Utilizes the EM approach to obtain a mixture
of log-concave densities. Utilizes Gaussian hierarchical clustering to
initilize the posterior probabilities of class affiliation (as proposed
by the package LogConcDEAD by Cule et al.).
fmlcdEM(X, K = 2, posterior, verbose = 0, maxIter = 50)Matrix of data points (one sample per row)
Number of latent variables (default: 2)
Matrix with posterior probabilities for class affiliation; Initialized if not provided using a Gaussian hierarchical clustering.
Int determining the verboseness of the code; 0 = no output to 3. (default: 0)
Maximal number of EM iterations. (default: 50)
Parametrization of the mixture density
List of length K, where each entry contains the hyperplane for one density
Matrix where each row contains the marginal distribution p(x)
Marginal distribution over the latent variable p(z)