Model Based Clustering for Mixed Data
Model based clustering for mixed data: clustMD
Model Based Clustering for Mixed Data
Model Based Clustering for Mixed Data
Run multiple clustMD models in parallel
Parallel coordinates plot adapted for clustMD output
Return the mean and covariance matrix of a truncated multivariate norm...
E-step of the (MC)EM algorithm
Extracts relevant output from clustMDparallel object
M-step of the (MC)EM algorithm
Calculate the mode of a sample
Calculates the number of free parameters for the clustMD model.
Approximates the observed log likelihood.
Check if response patterns are equal
Calculates the threshold parameters for ordinal variables.
Plotting method for objects of class clustMD
Summary plots for a clustMDparallel object
Print basic details of clustMD object.
Print basic details of clustMDparallel object
Helper internal function for dtmvnom()
Stable computation of the log of a sum
Summarise clustMD object
Prints a summary of a clustMDparallel object to screen.
Calculate the outer product of a vector with itself
Calculates the first and second moments of the latent data
Calculates the first and second moments of the latent data for diagona...
Transforms Monte Carlo simulated data into categorical data. Calculate...
Model-based clustering of mixed data (i.e. data which consist of continuous, binary, ordinal or nominal variables) using a parsimonious mixture of latent Gaussian variable models.