Interpretable nonparametric modeling of longitudinal data using additive Gaussian process regression. Contains functionality for inferring covariate effects and assessing covariate relevances. Models are specified using a convenient formula syntax, and can include shared, group-specific, non-stationary, heterogeneous and temporally uncertain effects. Bayesian inference for model parameters is performed using 'Stan' (rstan). The modeling approach and methods are described in detail in Timonen et al. (2021).

## Core functions

Main functionality of the package consists of creating and fitting an additive GP model:

• lgp: Specify and fit an additive GP model with one command.

• create_model: Define an lgpmodel object.

• sample_model: Fit a model by sampling the posterior distribution of its parameters and create an lgpfit object.

• pred: Computing model predictions and inferred covariate effects after fitting a model.

• relevances: Assessing covariate relevances after fitting a model.

• prior_pred: Prior predictive sampling to check if your prior makes sense.

## Visualization

• plot_pred: Plot model predictions.

• plot_components: Visualize inferred model components.

• plot_draws: Visualize parameter draws.

• plot_data: Visualize longitudinal data.

## Data

The data that you wish to analyze with 'lgpr' should be in an R data.frame where columns correspond to measured variables and rows correspond to observations. Some functions that can help working with such data frames are:

• new_x: Creating new test points where the posterior distribution of any function component or sum of all components, or the posterior predictive distribution can be computed after model fitting.

• Other functions: add_factor, add_factor_crossing, add_dis_age, adjusted_c_hat.

## Vignettes and tutorials

See https://jtimonen.github.io/lgpr-usage/index.html. The tutorials focus on code and use cases, whereas the Mathematical description of lgpr models vignette describes the statistical models and how they can be customized in 'lgpr'.

## Citation

Run citation("lgpr") to get citation information.

## Feedback

Bug reports, PRs, enhancement ideas or user experiences in general are welcome and appreciated. Create an issue in Github or email the author.

## References

1. Timonen, J. et al. (2021). lgpr: an interpretable non-parametric method for inferring covariate effects from longitudinal data. Bioinformatics, url.

2. Carpenter, B. et al. (2017). Stan: A probabilistic programming language. Journal of Statistical Software 76(1).

## Author

Juho Timonen (first.last at iki.fi)