Longitudinal Gaussian Process Regression
Easily add the disease-related age variable to a data frame
Easily add a categorical covariate to a data frame
Add a crossing of two factors to a data frame
Set the GP mean vector, taking TMM or other normalization into account
Apply variable scaling
Character representations of different formula objects
Parse the covariates and model components from given data and formula
Create a model formula
Parse the response variable and its likelihood model
Parse the given modeling options
Parse given prior
Create a model
Helper function for plots
Create a standardizing transform
Density and quantile functions of the inverse gamma distribution
Draw pseudo-observations from posterior or prior predictive distributi...
Quick way to create an example lgpfit, useful for debugging
Print a fit summary.
An S4 class to represent analytically computed predictive distribution...
Extract parameter draws from lgpfit or stanfit
Extract model predictions and function posteriors
Compute a kernel matrix (covariance matrix)
An S4 class to represent input for kernel matrix computations
Main function of the 'lgpr' package
An S4 class to represent an lgp expression
An S4 class to represent the output of the lgp function
An S4 class to represent an lgp formula
An S4 class to represent an additive GP model
The 'lgpr' package.
An S4 class to represent the right-hand side of an lgp formula
An S4 class to represent variable scaling
An S4 class to represent a data set simulated using the additive GP fo...
An S4 class to represent one formula term
Print a model summary.
Create test input points for prediction
Operations on formula terms and expressions
Plot a generated/fit model component
Plot longitudinal data and/or model fit so that each subject/group has...
Visualize all model components
Vizualizing longitudinal data
Visualize the distribution of parameter draws
Visualize input warping function with several steepness parameter valu...
Plot the inverse gamma-distribution pdf
Visualizing model predictions or inferred covariate effects
Visualize an lgpsim object (simulated data)
Graphical posterior predictive checks
Posterior predictions and function posteriors
An S4 class to represent prior or posterior draws from an additive fun...
Prior (predictive) sampling
Convert given prior to numeric format
Prior definitions
Function for reading the built-in proteomics data
Assess component relevances
S4 generics for lgpfit, lgpmodel, and other objects
Fitting a model
Select relevant components
Printing formula object info using the show generic
Simulate latent function components for longitudinal data analysis
Create an input data frame X for simulated data
Simulate noisy observations
Compute all kernel matrices when simulating data
Generate an artificial longitudinal data set
Split data into training and test sets
Validate S4 class objects
Variance masking function
Input warping function
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'. The modeling approach and methods are described in detail in Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.