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latrend (version 1.6.2)

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

354

Version

1.6.2

License

GPL (>= 2)

Issues

Pull Requests

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Repository

https://github.com/niekdt/latrend

Homepage

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

Maintainer

Niek Den Teuling

Last Published

July 4th, 2025

Functions in latrend (1.6.2)

createTestDataFold

Create the test fold data for validation

Get the cluster names

Select trajectories

clusterProportions

Proportional size of each cluster

.guessResponseVariable

Guess the response variable

confusionMatrix

Compute the posterior confusion matrix

Check model convergence

Extract lcModel fitted values

fittedTrajectories

Extract the fitted trajectories

lcMethod estimation step: compose an lcMethod object

generateLongData

Generate longitudinal test data

defineExternalMetric

Define an external metric for lcModels

createTrainDataFolds

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

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

getArgumentDefaults

Default argument values for the given method specification

Compute external model metric(s)

Extract lcModel coefficients

getExternalMetricNames

Get the names of the available external metrics

getInternalMetricDefinition

Get the internal metric definition

createTestDataFolds

Create all k test folds from the training data

Estimation time

df.residual.lcModel

Extract the residual degrees of freedom from a lcModel

Extract the trajectory identifier variable

Get the trajectory ids on which the model was fitted

.defineInternalDistanceMetrics

Define the distance metrics for multiple types at once

Get citation info

interface-mixtools

mixtools interface

defineInternalMetric

Define an internal metric for lcModels

formula.lcMethod

Extract formula

formula.lcModel

Extract the formula of a lcModel

deviance.lcModel

lcModel deviance

clusterTrajectories,lcModelPartition-method

function interface

getArgumentExclusions

Arguments to be excluded from the specification

Check if object is of Class

evaluate.lcMethod

Substitute the call arguments for their evaluated values

Get the method specification

Cluster longitudinal data using the specified method

Cluster longitudinal data for a list of method specifications

interface-mixtvem

mixtvem interface

interface-akmedoids

akmedoids interface

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

interface-crimCV

crimCV interface

getCall.lcModel

Get the model call

getInternalMetricNames

Get the names of the available internal metrics

latrend-generics

Generics used by latrend for different classes

latrend-estimation

Overview of lcMethod estimation functions

interface-featureBased

featureBased interface

lcMatrixMethod-class

interface-dtwclust

dtwclust interface

interface-flexmix

flexmix interface

getExternalMetricDefinition

Get the external metric definition

lcApproxModel-class

lcApproxModel class

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

Specify a longitudinal k-means (KML) method

getArgumentDefaults,lcMethodLcmmGMM-method

lcMethod-estimation

Longitudinal cluster method (lcMethod) estimation procedure

latrend-parallel

Parallel computation using latrend

interface-mclust

mclust interface

lcMethodAkmedoids

Specify AKMedoids method

lcMethodFunction

Specify a custom method based on a function

latrend-package

latrend: A Framework for Clustering Longitudinal Data

lcMethodLcmmGBTM

Specify GBTM method

Cluster longitudinal data repeatedly

lcMethodFlexmixGBTM

Group-based trajectory modeling using flexmix

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

Artificial longitudinal dataset comprising three classes

Method fit modifiers

lcMethodDtwclust

Specify time series clustering via dtwclust

interface-metaMethods

lcMetaMethod abstract class

Specify a random-partitioning method

Specify a zero-inflated repeated-measures GBTM method

lcMethodMixtoolsNPRM

Specify non-parametric estimation for independent repeated measures

[[,lcMethod-method

Retrieve and evaluate a lcMethod argument by name

interface-mixAK

mixAK interface

latrend-methods

Supported methods for longitudinal clustering

latrend-metrics

lcMethodLcmmGMM

Specify GMM method using lcmm

Extract the log-likelihood of a lcModel

lcModel class

Compute internal model metric(s)

lcModel-data-filters

Data filters for lcModel

lcMethodMixAK_GLMM

Specify a GLMM iwht a normal mixture in the random effects

lcMethodMclustLLPA

Longitudinal latent profile analysis

Specify a FunFEM method

Cluster longitudinal data using bootstrapping

Cluster longitudinal data over k folds

lcMethodStratify

Specify a stratification method

Select the lcModel with the lowest metric value

lcModelPartition

Create a lcModel with pre-defined partitioning

Generate a list of lcMethod objects

Cluster-handling functions for lcModel implementations.

plotFittedTrajectories

Plot the fitted trajectories

Plot one or more internal metrics for all lcModels

Longitudinal cluster result (lcModel)

predictForCluster

Predict trajectories conditional on cluster membership

Reduce the memory footprint of an object for serialization

predictPostprob

Posterior probability for new data

Extract residual standard deviation from a lcModel

lcModelWeightedPartition

Create a lcModel with pre-defined weighted partitioning

plot-lcModel-method

plotTrajectories

Plot the data trajectories

Number of trajectories

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

names,lcMethod-method

lcMethod argument names

Number of observations used for the lcModel fit

Quantile-quantile plot

Print lcModels list concisely

lcMethod estimation step: method argument validation logic

weighted.meanNA

Weighted arithmetic mean ignoring NAs

initialize,lcMethod-method

lcMethod initialization

Argument matching with defaults and parent ellipsis expansion

Extract the model training data

model.data.lcModel

Extract the model data that was used for fitting

residuals.lcModel

Extract lcModel residuals

model.frame.lcModel

Extract model training data

subset.lcModels

Subsetting a lcModels list based on method arguments

Convert a multiple time series matrix to a data.frame

summary.lcModel

Summarize a lcModel

lcMethodFeature

Feature-based clustering

lcMethodMixTVEM

Specify a MixTVEM

interface-funFEM

funFEM interface

postprobFromAssignments

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

Number of clusters

latrend-approaches

High-level approaches to longitudinal clustering

Longitudinal dataset representation

lcMethodFlexmix

Method interface to flexmix()

Posterior probability per fitted trajectory

predict.lcModel

lcModel predictions

trajectoryAssignments

Get the cluster membership of each trajectory

Sampling times of a lcModel

lcMethod estimation step: method preparation logic

postProbFromObs

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

responseVariable

Extract response variable

lcMethodMixtoolsGMM

Specify mixed mixture regression model using mixtools

lcModels: a list of lcModel objects

Get the trajectories

predictAssignments

Predict the cluster assignments for new trajectories

update.lcMethod

Update a method specification

Update a lcModel

Extract the time variable

transformPredict

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

transformFitted

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

plot-lcModels-method

Grid plot for a list of models

Construct a list of lcModel objects

Mean ignoring NAs

plotClusterTrajectories

Plot cluster trajectories

Select the lcModel with the highest metric value

Convert a longitudinal data.frame to a matrix

Sample an index of a vector weighted by the elements

lcMethod estimation step: logic for preparing the training data

Print the arguments of an lcMethod object

Test the implementation of an lcMethod and associated lcModel subclasses

Test a condition

as.data.frame.lcMethods

Convert a list of lcMethod objects to a data.frame

Convert a list of lcModels to a lcModels list

Biweekly Mean PAP Therapy Adherence of OSA Patients over 1 Year

Convert a list of lcMethod objects to a lcMethods list

Average posterior probability of assignment (APPA)

as.data.frame.lcModels

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

as.data.frame.lcMethod

Convert lcMethod arguments to a list of atomic types

Odds of correct classification (OCC)

as.list.lcMethod

Extract the method arguments as a list

clusterTrajectories

Extract cluster trajectories

Number of trajectories per cluster

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

Update the cluster names

latrend-specific assertions