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gppm (version 0.3.0)

Gaussian Process Panel Modeling

Description

Provides an implementation of Gaussian process panel modeling (GPPM). GPPM is described in Karch, Brandmaier & Voelkle (2020; ) and Karch (2016; ). Essentially, GPPM is Gaussian process based modeling of longitudinal panel data. 'gppm' also supports regular Gaussian process regression (with a focus on flexible model specification), and multi-task learning.

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Install

install.packages('gppm')

Monthly Downloads

40

Version

0.3.0

License

GPL-3 | file LICENSE

Issues

1

Pull Requests

0

Stars

3

Forks

4

Maintainer

Julian Karch

Last Published

August 25th, 2025

Functions in gppm (0.3.0)

vcov.GPPM

Variance-Covariance Matrix
trueParas

Parameters used for generating demoLGCM.
confint.GPPM

Confidence Intervals
covFun

Covariance Function
coef.GPPM

Point Estimates
demoLGCM

Simulated Data From a Latent Growth Curve Model.
accuracy

Accuracy Estimates for Predictions
fit.GPPM

Fit a Gaussian process panel model
createLeavePersonsOutFolds

Create Leave-persons-out Folds
logLik.GPPM

Log-Likelihood
fit

Generic Method For Fitting a model
SE

Standard Errors
meanFun

Mean Function
crossvalidate

Cross-validation.
nPars

Number of Parameters
gppm

Define a Gaussian process panel model
getIntern

Generic Extraction Function
gppmControl

Define settings for a Gaussian process panel model
summary.GPPM

Summarizing GPPM
fitted.GPPM

Person-specific mean vectors and covariance matrices
simulate.GPPM

Simulate from a Gaussian process panel model
nObs

Number of Observations
getData

Data Set
maxNObs

Maximum Number of Observations per Person
predict.GPPM

GPPM predictions
pars

Parameter Names
plot.GPPMPred

Plotting predictions
parEsts

Essential Parameter Estimation Results
nPers

Number of persons
nPreds

Number of Predictors
plot.LongData

Plot a Long Data Frame
preds

Predictors Names