Package: LCAextend 1.3

Alexandre BUREAU

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

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.

Authors:Arafat TAYEB <[email protected]>, Alexandre BUREAU <[email protected]> and Aurelie Labbe <[email protected]>

LCAextend_1.3.tar.gz
LCAextend_1.3.tar.gz(r-4.5-noble)LCAextend_1.3.tar.gz(r-4.4-noble)
LCAextend_1.3.tgz(r-4.4-emscripten)
LCAextend.pdf |LCAextend.html✨
LCAextend/json (API)

# Install 'LCAextend' in R:
install.packages('LCAextend', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • 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

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.52 score 33 scripts 124 downloads 27 exports 80 dependencies

Last updated 6 years agofrom:d31227453a. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 22 2024
R-4.5-linuxOKDec 22 2024

Exports:alpha.computeattrib.densdens.normdens.prod.ordidownwarddownward.connecte.stepinit.norminit.ordiinit.p.translca.modelmodel.selectn.paramoptim.const.ordioptim.diff.normoptim.equal.normoptim.gene.normoptim.indep.normoptim.noconst.ordioptim.probsp.computep.post.childp.post.foundupwardupward.connectweight.famdepweight.nuc

Dependencies:backportsbase64encbootbslibcachemcheckmatecliclustercodetoolscolorspacedata.tabledigestevaluatefansifarverfastmapfontawesomeforeignFormulafsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonlitekinship2knitrlabelinglatticelifecyclemagrittrMASSMatrixMatrixModelsmemoisemgcvmimemultcompmunsellmvtnormnlmennetpillarpkgconfigpolsplinequadprogquantregR6rappdirsRColorBrewerrlangrmarkdownrmsrpartrstudioapisandwichsassscalesSparseMstringistringrsurvivalTH.datatibbletinytexutf8vctrsviridisviridisLitewithrxfunyamlzoo

Readme and manuals

Help Manual

Help pageTopics
computes cumulative logistic coefficients using probabilitiesalpha.compute
associates to a function of density parameter optimization an attribute to distinguish between ordinal and normal casesattrib.dens
computes the multinormal density of a given continuous measurement vector for all classesdens.norm
computes the probability of a given discrete measurement vector for all classes under a product of multinomialdens.prod.ordi
performs the downward step of the peeling algorithm and computes unnormalized triplet and individual weightsdownward
performs a downward step for a connectordownward.connect
performs the E step of the EM algorithm for a single pedigree for both cases with and without familial dependencee.step
computes initial values for the EM algorithm in the case of continuous measurementsinit.norm
computes the initial values for EM algorithm in the case of ordinal measurementsinit.ordi
initializes the transition probabilitiesinit.p.trans
fits latent class models for phenotypic measurements in pedigrees with or without familial dependence using an Expectation-Maximization (EM) algorithmlca.model
selects a latent class model for pedigree datamodel.select
computes the number of parameters of a modeln.param
performs the M step for the measurement distribution parameters in multinomial case, with an ordinal constraint on the parametersoptim.const.ordi
performs the M step for measurement density parameters in multinormal caseoptim.diff.norm
performs the M step for measurement density parameters in multinormal caseoptim.equal.norm
performs the M step for measurement density parameters in multinormal caseoptim.gene.norm
performs the M step for measurement density parameters in multinormal caseoptim.indep.norm
performs the M step for the measurement distribution parameters in multinomial case without constraint on the parametersoptim.noconst.ordi
performs the M step of the EM algorithm for the probability parametersoptim.probs
computes the probability vector using logistic coefficientsp.compute
computes the posterior probability of observations of a childp.post.child
computes the posterior probability of observations of a founderp.post.found
parameters to be used for examples in the case of continuous measurementsparam.cont
parameters to be used for examples in the case of discrete or ordinal measurementsparam.ordi
pedigrees with continuous data to be used for examplesped.cont
pedigrees with discrete or ordinal data to be used for examplesped.ordi
peeling order of pedigrees and couples in pedigreespeel
probabilities parameters to be used for examplesprobs
performs the upward step of the peeling algorithm of a pedigreeupward
performs the upward step for a connectorupward.connect
performs the computation of triplet and individual weights for a pedigree under familial dependenceweight.famdep
performs the computation of unnormalized triplet and individuals weights for a nuclear family in the pedigreeweight.nuc