LCAextend-package       Latent Class Analysis (LCA) and model selection
                        for pedigree data
alpha.compute           computes cumulative logistic coefficients using
                        probabilities
attrib.dens             associates to a function of density parameter
                        optimization an attribute to distinguish
                        between ordinal and normal cases
dens.norm               computes the multinormal density of a given
                        continuous measurement vector for all classes
dens.prod.ordi          computes the probability of a given discrete
                        measurement vector for all classes under a
                        product of multinomial
downward                performs the downward step of the peeling
                        algorithm and computes unnormalized triplet and
                        individual weights
downward.connect        performs a downward step for a connector
e.step                  performs the E step of the EM algorithm for a
                        single pedigree for both cases with and without
                        familial dependence
init.norm               computes initial values for the EM algorithm in
                        the case of continuous measurements
init.ordi               computes the initial values for EM algorithm in
                        the case of ordinal measurements
init.p.trans            initializes the transition probabilities
lca.model               fits latent class models for phenotypic
                        measurements in pedigrees with or without
                        familial dependence using an
                        Expectation-Maximization (EM) algorithm
model.select            selects a latent class model for pedigree data
n.param                 computes the number of parameters of a model
optim.const.ordi        performs the M step for the measurement
                        distribution parameters in multinomial case,
                        with an ordinal constraint on the parameters
optim.diff.norm         performs the M step for measurement density
                        parameters in multinormal case
optim.equal.norm        performs the M step for measurement density
                        parameters in multinormal case
optim.gene.norm         performs the M step for measurement density
                        parameters in multinormal case
optim.indep.norm        performs the M step for measurement density
                        parameters in multinormal case
optim.noconst.ordi      performs the M step for the measurement
                        distribution parameters in multinomial case
                        without constraint on the parameters
optim.probs             performs the M step of the EM algorithm for the
                        probability parameters
p.compute               computes the probability vector using logistic
                        coefficients
p.post.child            computes the posterior probability of
                        observations of a child
p.post.found            computes the posterior probability of
                        observations of a founder
param.cont              parameters to be used for examples in the case
                        of continuous measurements
param.ordi              parameters to be used for examples in the case
                        of discrete or ordinal measurements
ped.cont                pedigrees with continuous data to be used for
                        examples
ped.ordi                pedigrees with discrete or ordinal data to be
                        used for examples
peel                    peeling order of pedigrees and couples in
                        pedigrees
probs                   probabilities parameters to be used for
                        examples
upward                  performs the upward step of the peeling
                        algorithm of a pedigree
upward.connect          performs the upward step for a connector
weight.famdep           performs the computation of triplet and
                        individual weights for a pedigree under
                        familial dependence
weight.nuc              performs the computation of unnormalized
                        triplet and individuals weights for a nuclear
                        family in the pedigree
