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latrend (version 1.6.3)

A Framework for Clustering Longitudinal Data

Description

A framework for clustering longitudinal datasets in a standardized way. The package provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. The interface enables users to compare results between methods, and to implement and evaluate new methods with ease. The 'akmedoids' package is available from .

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Install

install.packages('latrend')

Monthly Downloads

342

Version

1.6.3

License

GPL (>= 2)

Issues

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Repository

https://github.com/niekdt/latrend

Homepage

https://niekdt.github.io/latrend/

Maintainer

Niek Den Teuling

Last Published

February 13th, 2026

Functions in latrend (1.6.3)

Number of trajectories per cluster

Check model convergence

Get the cluster names

latrend-specific assertions

clusterTrajectories

Extract cluster trajectories

confusionMatrix

Compute the posterior confusion matrix

Extract lcModel coefficients

lcMethod estimation step: compose an lcMethod object

clusterProportions

Proportional size of each cluster

Update the cluster names

createTestDataFold

Create the test fold data for validation

createTestDataFolds

Create all k test folds from the training data

.defineInternalDistanceMetrics

Define the distance metrics for multiple types at once

deviance.lcModel

lcModel deviance

defineInternalMetric

Define an internal metric for lcModels

defineExternalMetric

Define an external metric for lcModels

df.residual.lcModel

Extract the residual degrees of freedom from a lcModel

createTrainDataFolds

Create the training data for each of the k models in k-fold cross validation evaluation

Select trajectories

.guessResponseVariable

Guess the response variable

Estimation time

fittedTrajectories

Extract the fitted trajectories

formula.lcMethod

Extract formula

formula.lcModel

Extract the formula of a lcModel

generateLongData

Generate longitudinal test data

evaluate.lcMethod

Substitute the call arguments for their evaluated values

Compute external model metric(s)

Extract lcModel fitted values

getArgumentDefaults

Default argument values for the given method specification

lcMethod estimation step: logic for fitting the method to the processed data

getCall.lcModel

Get the model call

Get the method specification

getExternalMetricNames

Get the names of the available external metrics

getExternalMetricDefinition

Get the external metric definition

getInternalMetricDefinition

Get the internal metric definition

Get citation info

getArgumentExclusions

Arguments to be excluded from the specification

getInternalMetricNames

Get the names of the available internal metrics

interface-akmedoids

akmedoids interface

interface-featureBased

featureBased interface

clusterTrajectories,lcModelPartition-method

function interface

Get the trajectory ids on which the model was fitted

Extract the trajectory identifier variable

[[,lcMethod-method

Retrieve and evaluate a lcMethod argument by name

interface-crimCV

crimCV interface

interface-dtwclust

dtwclust interface

initialize,lcMethod-method

lcMethod initialization

interface-flexmix

flexmix interface

interface-mixtvem

mixtvem interface

interface-metaMethods

lcMetaMethod abstract class

getArgumentDefaults,lcMethodLcmmGMM-method

interface-mixtools

mixtools interface

interface-funFEM

funFEM interface

Check if object is of Class

Check whether the argument of a lcMethod has a defined value.

interface-mixAK

mixAK interface

interface-mclust

mclust interface

latrend-estimation

Overview of lcMethod estimation functions

Cluster longitudinal data using the specified method

latrend-generics

Generics used by latrend for different classes

latrend-parallel

Parallel computation using latrend

Longitudinal dataset representation

latrend-package

latrend: A Framework for Clustering Longitudinal Data

latrend-approaches

High-level approaches to longitudinal clustering

Cluster longitudinal data for a list of method specifications

latrend-methods

Supported methods for longitudinal clustering

latrend-metrics

Artificial longitudinal dataset comprising three classes

lcMethod-estimation

Longitudinal cluster method (lcMethod) estimation procedure

lcMethodAkmedoids

Specify AKMedoids method

lcApproxModel-class

lcApproxModel class

Cluster longitudinal data using bootstrapping

Cluster longitudinal data repeatedly

Method fit modifiers

Cluster longitudinal data over k folds

lcMatrixMethod-class

Specify a longitudinal k-means (KML) method

Two-step clustering through latent growth curve modeling and k-means

lcMethodFunction

Specify a custom method based on a function

Two-step clustering through linear regression modeling and k-means

lcMethodFlexmixGBTM

Group-based trajectory modeling using flexmix

Specify a zero-inflated repeated-measures GBTM method

lcMethodDtwclust

Specify time series clustering via dtwclust

lcMethodFlexmix

Method interface to flexmix()

Specify a FunFEM method

lcMethodFeature

Feature-based clustering

lcMethodMixtoolsGMM

Specify mixed mixture regression model using mixtools

lcMethodMixAK_GLMM

Specify a GLMM iwht a normal mixture in the random effects

lcMethodStratify

Specify a stratification method

Specify a random-partitioning method

lcMethodMclustLLPA

Longitudinal latent profile analysis

Generate a list of lcMethod objects

lcMethodMixtoolsNPRM

Specify non-parametric estimation for independent repeated measures

lcMethodLcmmGBTM

Specify GBTM method

lcMethodLcmmGMM

Specify GMM method using lcmm

lcMethodMixTVEM

Specify a MixTVEM

Construct a list of lcModel objects

lcModelWeightedPartition

Create a lcModel with pre-defined weighted partitioning

Cluster-handling functions for lcModel implementations.

lcModel class

Extract the log-likelihood of a lcModel

lcModels: a list of lcModel objects

lcModelPartition

Create a lcModel with pre-defined partitioning

Longitudinal cluster result (lcModel)

lcModel-data-filters

Data filters for lcModel

Argument matching with defaults and parent ellipsis expansion

model.data.lcModel

Extract the model data that was used for fitting

Extract the model training data

Number of trajectories

Select the lcModel with the highest metric value

Select the lcModel with the lowest metric value

names,lcMethod-method

lcMethod argument names

Number of clusters

model.frame.lcModel

Extract model training data

Compute internal model metric(s)

Mean ignoring NAs

Plot one or more internal metrics for all lcModels

plotClusterTrajectories

Plot cluster trajectories

plot-lcModels-method

Grid plot for a list of models

plotFittedTrajectories

Plot the fitted trajectories

plotTrajectories

Plot the data trajectories

lcMethod estimation step: logic for post-processing the fitted lcModel

postProbFromObs

Compute the id-specific postprob matrix from a given observation-level postprob matrix

Posterior probability per fitted trajectory

Number of observations used for the lcModel fit

plot-lcModel-method

postprobFromAssignments

Create a posterior probability matrix from a vector of cluster assignments.

lcMethod estimation step: logic for preparing the training data

predict.lcModel

lcModel predictions

predictForCluster

Predict trajectories conditional on cluster membership

Quantile-quantile plot

Print the arguments of an lcMethod object

Print lcModels list concisely

predictAssignments

Predict the cluster assignments for new trajectories

lcMethod estimation step: method preparation logic

predictPostprob

Posterior probability for new data

summary.lcModel

Summarize a lcModel

subset.lcModels

Subsetting a lcModels list based on method arguments

Sampling times of a lcModel

Extract residual standard deviation from a lcModel

residuals.lcModel

Extract lcModel residuals

Reduce the memory footprint of an object for serialization

Extract the time variable

responseVariable

Extract response variable

Test the implementation of an lcMethod and associated lcModel subclasses

Test a condition

update.lcMethod

Update a method specification

Convert a multiple time series matrix to a data.frame

trajectoryAssignments

Get the cluster membership of each trajectory

Convert a longitudinal data.frame to a matrix

Get the trajectories

lcMethod estimation step: method argument validation logic

transformPredict

Helper function for custom lcModel classes implementing predict.lcModel()

weighted.meanNA

Weighted arithmetic mean ignoring NAs

Update a lcModel

transformFitted

Helper function for custom lcModel classes implementing fitted.lcModel()

Sample an index of a vector weighted by the elements

as.data.frame.lcModels

Generate a data.frame containing the argument values per method per row

Weekly Mean PAP Therapy Usage of OSA Patients in the First 3 Months

Odds of correct classification (OCC)

as.data.frame.lcMethods

Convert a list of lcMethod objects to a data.frame

Average posterior probability of assignment (APPA)

Convert a list of lcMethod objects to a lcMethods list

as.list.lcMethod

Extract the method arguments as a list

as.data.frame.lcMethod

Convert lcMethod arguments to a list of atomic types

Convert a list of lcModels to a lcModels list

Biweekly Mean PAP Therapy Adherence of OSA Patients over 1 Year