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| accuracy | Accuracy Estimates for Predictions |
| coef.GPPM | Point Estimates |
| confint.GPPM | Confidence Intervals |
| covf | Covariance Function |
| createLeavePersonsOutFolds | Create Leave-persons-out Folds |
| crossvalidate | Cross-validation. |
| datas | Data Set |
| demoLGCM | Simulated Data From a Latent Growth Curve Model. |
| fit | Generic Method For Fitting a model |
| fit.GPPM | Fit a Gaussian process panel model |
| fitted.GPPM | Person-specific mean vectors and covariance matrices |
| getIntern | Generic Extraction Function |
| gppm | Define a Gaussian process panel model |
| gppmControl | Define settings for a Gaussian process panel model |
| logLik.GPPM | Log-Likelihood |
| maxnObs | Maximum Number of Observations per Person |
| meanf | Mean Function |
| nObs | Number of Observations |
| nPars | Number of Parameters |
| nPers | Number of persons |
| nPreds | Number of Predictors |
| parEsts | Essential Parameter Estimation Results |
| pars | Parameter Names |
| plot.GPPMPred | Plotting predictions |
| plot.LongData | Plot a Long Data Frame |
| predict.GPPM | GPPM predictions |
| preds | Predictors Names |
| print.summary.GPPM | Summarizing GPPM |
| SE | Standard Errors |
| simulate.GPPM | Simulate from a Gaussian process panel model |
| summary.GPPM | Summarizing GPPM |
| trueParas | Parameters used for generating 'demoLGCM'. |
| vcov.GPPM | Variance-Covariance Matrix |