R: Latent Class Analysis (LCA) with Familial Dependence in Extended Pedigrees

# Latent Class Analysis (LCA) with Familial Dependence in Extended Pedigrees

## Documentation for package ‘LCAextend’ version 1.3

- DESCRIPTION file

## Help Pages

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