Latent Class Analysis (LCA) with Familial Dependence in Extended Pedigrees
performs the M step for measurement density parameters in multinormal ...
performs the M step for measurement density parameters in multinormal ...
computes cumulative logistic coefficients using probabilities
associates to a function of density parameter optimization an attribut...
computes the multinormal density of a given continuous measurement vec...
computes the probability of a given discrete measurement vector for al...
performs a downward step for a connector
performs the downward step of the peeling algorithm and computes unnor...
performs the E step of the EM algorithm for a single pedigree for both...
computes initial values for the EM algorithm in the case of continuous...
computes the initial values for EM algorithm in the case of ordinal me...
initializes the transition probabilities
fits latent class models for phenotypic measurements in pedigrees with...
selects a latent class model for pedigree data
computes the number of parameters of a model
performs the M step for the measurement distribution parameters in mul...
performs the M step for measurement density parameters in multinormal ...
performs the M step for measurement density parameters in multinormal ...
performs the M step for the measurement distribution parameters in mul...
performs the M step of the EM algorithm for the probability parameters
computes the probability vector using logistic coefficients
computes the posterior probability of observations of a child
computes the posterior probability of observations of a founder
performs the upward step for a connector
performs the upward step of the peeling algorithm of a pedigree
performs the computation of triplet and individual weights for a pedig...
performs the computation of unnormalized triplet and individuals weigh...
Latent Class Analysis of phenotypic measurements in pedigrees and model selection based on one of two methods: likelihood-based cross-validation and Bayesian Information Criterion. Computation of individual and triplet child-parents weights in a pedigree is performed using an upward-downward algorithm. The model takes into account the familial dependence defined by the pedigree structure by considering that a class of a child depends on his parents classes via triplet-transition probabilities of the classes. The package handles the case where measurements are available on all subjects and the case where measurements are available only on symptomatic (i.e. affected) subjects. Distributions for discrete (or ordinal) and continuous data are currently implemented. The package can deal with missing data.