latrend

Convert lcMethod arguments to a list of atomic types

Convert a list of lcMethod objects to a data.frame

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

Convert a list of lcMethod objects to a lcMethods list

Convert a list of lcModels to a lcModels list

Extract the method arguments as a list

latrend-specific assertions

Retrieve and evaluate a lcMethod argument by name

Get the cluster names

Update the cluster names

Proportional size of each cluster

Number of strata per cluster

Extract the cluster trajectories

Coefficients of a lcModel

Compute the posterior confusion matrix

Check model convergence

Create the test fold data for validation

Create all k test folds from the training data

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

Cast a longitudinal data.frame to a matrix

Define an external metric for lcModels

Define an internal metric for lcModels

lcModel deviance

Extract the residual degrees of freedom from a lcModel

Get the model estimation time

Substitute the call arguments for their evaluated values

Compute external model metric(s)

Extract lcModel fitted values

Extract formula

Extract the formula of a lcModel

Generate longitudinal test data

Get the model call

Get the external metric definition

Get the names of the available external metrics

Get the internal metric definition

Get the names of the available internal metrics

Get the method specification of a lcModel

Extract the trajectory identifier variable

Get the unique ids included in this model

Retrieve and evaluate a lcMethod argument by name

akmedoids interface

crimCV interface

custom interface

dtwclust interface

featureBased interface

flexmix interface

funFEM interface

kml interface

lcmm interface

longclust interface

mclust interface

mixAK interface

mixtools interface

mixtvem interface

Check if object is of Class

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

Cluster longitudinal data

Method- and model-specific generics defined by the latrend package

latrend: A Framework for Clustering Longitudinal Data

Parallel computing using latrend

Cluster longitudinal data for a list of model specifications

Cluster longitudinal data using bootstrapping

Cluster longitudinal data over k folds

Synthetic longitudinal dataset comprising three classes

Cluster longitudinal data repeatedly

lcApproxModel class

lcMatrixMethod

Create a lcMethod object of the specified type and arguments

lcMethod class

Create a lcMethod object from a call

Specify AKMedoids method

Specify a zero-inflated repeated-measures GBTM method

Specify a custom method based on a model function

Specify time series clustering via dtwclust

Feature-based clustering

Method interface to flexmix()

Group-based trajectory modeling using flexmix

Specify a FunFEM method

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

Specify a longitudinal k-means (KML) method

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

Specify GBTM method

Specify GMM method using lcmm

Specify Longclust method

Longitudinal latent profile analysis

Specify a GLMM iwht a normal mixture in the random effects

Specify a MixTVEM

Specify mixed mixture regression model using mixtools

Specify non-parametric estimation for independent repeated measures

Specify a random-partitioning method

Specify a stratification method

Generate a list of lcMethod objects

lcModel class

Data filters for lcModel

Cluster-handling functions for lcModel implementations.

Specify a model based on a pre-computed result.

Create a lcModel with pre-defined partitioning

Create a lcModel with pre-defined weighted partitioning

Construct a flat (named) list of lcModel objects

Extract the log-likelihood of a lcModel

Argument matching with defaults and parent ellipsis expansion

Select the lcModel with the highest metric value

Convert a repeated measures data matrix to a data.frame

Compute internal model metric(s)

Select the lcModel with the lowest metric value

Extract the model training data

Extract the model data that was used for fitting

Extract model training data

Number of clusters

Number of strata

Extract the number of observations from a lcModel

Plot a lcModel

Plot cluster trajectories

Plot one or more internal metrics for all lcModels

Plot trajectories

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

Posterior probability per fitted id

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

lcModel predictions

Predict the cluster assignments for new trajectories

lcModel prediction for a specific cluster

lcModel posterior probability prediction

Print the arguments of an lcMethod object

Print lcModels list concisely

Quantile-quantile plot

Extract lcModel residuals

Extract the response variable

Extract residual standard deviation from a lcModel

Strip a lcModel for serialization

Subsetting a lcModels list based on method arguments

Summarize a lcModel

Sampling times of a lcModel

Extract the time variable

Extract the fitted trajectories for all strata

Get the cluster membership of each trajectory

Helper function for ensuring the right fitted() output

Transform latrend input data into the right format

Helper function that matches the output to the specified newdata

Update a method specification

Update a lcModel

Weighted arithmetic mean ignoring NAs

Sample an index of a vector weighted by the elements