Model Based Functional Data Analysis

Documentation for package ‘MFDA’ version 1.1-1

check.start	Check starting point
compute.center	Update parameters for cluster k in M-step for functional mixture models.
compute.M.all	M-step in the EM algorithm for functional mixture models.
compute.M.pk	Compute the cluster proportion.
compute.reject	The rejection controlled EM.
compute.weight	Compute the weight for the penalized likelihood in M-step.
em.bic	BIC for Functional Mixture Gaussian Models
em.clust	Model-Based Clustering for a single Markov chain
Estep.tik	Compute the conditional probability of subject i in cluster k in E-step for functional mixture models.
Estep.tk	Compute the cluster proportion in E-step for functional mixture models.
MFclust	Model-Based Functional Data Clustering
MFclust.compute	Computational Component in Model-Based Functional Data Clustering
mkrandom	Generating Random Effects for cluster model
testdata	Time course simulated data