APPA                    Average posterior probability of assignment
                        (APPA)
OCC                     Odds of correct classification (OCC)
PAP.adh                 Weekly Mean PAP Therapy Usage of OSA Patients
                        in the First 3 Months
PAP.adh1y               Biweekly Mean PAP Therapy Adherence of OSA
                        Patients over 1 Year
[[,lcMethod-method      Retrieve and evaluate a lcMethod argument by
                        name
as.data.frame.lcMethod
                        Convert lcMethod arguments to a list of atomic
                        types
as.data.frame.lcMethods
                        Convert a list of lcMethod objects to a
                        data.frame
as.data.frame.lcModels
                        Generate a data.frame containing the argument
                        values per method per row
as.lcMethods            Convert a list of lcMethod objects to a
                        lcMethods list
as.lcModels             Convert a list of lcModels to a lcModels list
as.list.lcMethod        Extract the method arguments as a list
clusterNames            Get the cluster names
clusterNames<-          Update the cluster names
clusterProportions      Proportional size of each cluster
clusterSizes            Number of trajectories per cluster
clusterTrajectories     Extract cluster trajectories
coef.lcModel            Extract lcModel coefficients
compose                 'lcMethod' estimation step: compose an lcMethod
                        object
confusionMatrix         Compute the posterior confusion matrix
converged               Check model convergence
createTestDataFold      Create the test fold data for validation
createTestDataFolds     Create all k test folds from the training data
createTrainDataFolds    Create the training data for each of the k
                        models in k-fold cross validation evaluation
defineExternalMetric    Define an external metric for lcModels
defineInternalMetric    Define an internal metric for lcModels
deviance.lcModel        lcModel deviance
df.residual.lcModel     Extract the residual degrees of freedom from a
                        lcModel
estimationTime          Estimation time
evaluate.lcMethod       Substitute the call arguments for their
                        evaluated values
externalMetric          Compute external model metric(s)
fit                     'lcMethod' estimation step: logic for fitting
                        the method to the processed data
fitted.lcModel          Extract lcModel fitted values
fittedTrajectories      Extract the fitted trajectories
formula.lcMethod        Extract formula
formula.lcModel         Extract the formula of a lcModel
generateLongData        Generate longitudinal test data
getArgumentDefaults     Default argument values for the given method
                        specification
getArgumentExclusions   Arguments to be excluded from the specification
getCitation             Get citation info
getExternalMetricDefinition
                        Get the external metric definition
getExternalMetricNames
                        Get the names of the available external metrics
getInternalMetricDefinition
                        Get the internal metric definition
getInternalMetricNames
                        Get the names of the available internal metrics
getLabel                Object label
getLcMethod             Get the method specification
getName                 Object name
idVariable              Extract the trajectory identifier variable
ids                     Get the trajectory ids on which the model was
                        fitted
initialize,lcMethod-method
                        lcMethod initialization
interface-metaMethods   lcMetaMethod abstract class
latrend                 Cluster longitudinal data using the specified
                        method
latrend-approaches      High-level approaches to longitudinal
                        clustering
latrend-data            Longitudinal dataset representation
latrend-estimation      Overview of *'lcMethod'* estimation functions
latrend-generics        Generics used by latrend for different classes
latrend-methods         Supported methods for longitudinal clustering
latrend-metrics         Metrics
latrend-package         latrend: A Framework for Clustering
                        Longitudinal Data
latrend-parallel        Parallel computation using latrend
latrendBatch            Cluster longitudinal data for a list of method
                        specifications
latrendBoot             Cluster longitudinal data using bootstrapping
latrendCV               Cluster longitudinal data over k folds
latrendData             Artificial longitudinal dataset comprising
                        three classes
latrendRep              Cluster longitudinal data repeatedly
lcApproxModel-class     lcApproxModel class
lcFitMethods            Method fit modifiers
lcMethod-class          lcMethod class
lcMethod-estimation     Longitudinal cluster method ('lcMethod')
                        estimation procedure
lcMethodAkmedoids       Specify AKMedoids method
lcMethodCrimCV          Specify a zero-inflated repeated-measures GBTM
                        method
lcMethodDtwclust        Specify time series clustering via dtwclust
lcMethodFeature         Feature-based clustering
lcMethodFlexmix         Method interface to flexmix()
lcMethodFlexmixGBTM     Group-based trajectory modeling using flexmix
lcMethodFunFEM          Specify a FunFEM method
lcMethodFunction        Specify a custom method based on a function
lcMethodGCKM            Two-step clustering through latent growth curve
                        modeling and k-means
lcMethodKML             Specify a longitudinal k-means (KML) method
lcMethodLMKM            Two-step clustering through linear regression
                        modeling and k-means
lcMethodLcmmGBTM        Specify GBTM method
lcMethodLcmmGMM         Specify GMM method using lcmm
lcMethodMclustLLPA      Longitudinal latent profile analysis
lcMethodMixAK_GLMM      Specify a GLMM iwht a normal mixture in the
                        random effects
lcMethodMixTVEM         Specify a MixTVEM
lcMethodMixtoolsGMM     Specify mixed mixture regression model using
                        mixtools
lcMethodMixtoolsNPRM    Specify non-parametric estimation for
                        independent repeated measures
lcMethodRandom          Specify a random-partitioning method
lcMethodStratify        Specify a stratification method
lcMethods               Generate a list of lcMethod objects
lcModel                 Longitudinal cluster result (*'lcModel'*)
lcModel-class           'lcModel' class
lcModelPartition        Create a lcModel with pre-defined partitioning
lcModelWeightedPartition
                        Create a lcModel with pre-defined weighted
                        partitioning
lcModels                Construct a list of 'lcModel' objects
lcModels-class          'lcModels': a list of 'lcModel' objects
logLik.lcModel          Extract the log-likelihood of a lcModel
max.lcModels            Select the lcModel with the highest metric
                        value
metric                  Compute internal model metric(s)
min.lcModels            Select the lcModel with the lowest metric value
model.data.lcModel      Extract the model data that was used for
                        fitting
model.frame.lcModel     Extract model training data
nClusters               Number of clusters
nIds                    Number of trajectories
names,lcMethod-method   lcMethod argument names
nobs.lcModel            Number of observations used for the lcModel fit
plot-lcModel-method     Plot a lcModel
plot-lcModels-method    Grid plot for a list of models
plotClusterTrajectories
                        Plot cluster trajectories
plotFittedTrajectories
                        Plot the fitted trajectories
plotMetric              Plot one or more internal metrics for all
                        lcModels
plotTrajectories        Plot the data trajectories
postFit                 'lcMethod' estimation step: logic for
                        post-processing the fitted lcModel
postprob                Posterior probability per fitted trajectory
postprobFromAssignments
                        Create a posterior probability matrix from a
                        vector of cluster assignments.
preFit                  'lcMethod' estimation step: method preparation
                        logic
predict.lcModel         lcModel predictions
predictAssignments      Predict the cluster assignments for new
                        trajectories
predictForCluster       Predict trajectories conditional on cluster
                        membership
predictPostprob         Posterior probability for new data
prepareData             'lcMethod' estimation step: logic for preparing
                        the training data
print.lcMethod          Print the arguments of an lcMethod object
print.lcModels          Print lcModels list concisely
qqPlot                  Quantile-quantile plot
residuals.lcModel       Extract lcModel residuals
responseVariable        Extract response variable
sigma.lcModel           Extract residual standard deviation from a
                        lcModel
strip                   Reduce the memory footprint of an object for
                        serialization
subset.lcModels         Subsetting a lcModels list based on method
                        arguments
summary.lcModel         Summarize a lcModel
test.latrend            Test the implementation of an lcMethod and
                        associated lcModel subclasses
time.lcModel            Sampling times of a lcModel
timeVariable            Extract the time variable
trajectories            Get the trajectories
trajectoryAssignments   Get the cluster membership of each trajectory
transformFitted         Helper function for custom lcModel classes
                        implementing fitted.lcModel()
transformPredict        Helper function for custom lcModel classes
                        implementing predict.lcModel()
tsframe                 Convert a multiple time series matrix to a
                        data.frame
tsmatrix                Convert a longitudinal data.frame to a matrix
update.lcMethod         Update a method specification
update.lcModel          Update a lcModel
validate                'lcMethod' estimation step: method argument
                        validation logic
which.weight            Sample an index of a vector weighted by the
                        elements
