| The 'lgpr' package. | lgpr-package lgpr |
| Easily add the disease-related age variable to a data frame | add_dis_age |
| Easily add a categorical covariate to a data frame | add_factor |
| Add a crossing of two factors to a data frame | add_factor_crossing |
| Set the GP mean vector, taking TMM or other normalization into account | adjusted_c_hat |
| Apply variable scaling | apply_scaling |
| Character representations of different formula objects | as.character,lgpexpr-method as.character,lgpformula-method as.character,lgpterm-method as_character |
| Create a model | create_model |
| Parse the covariates and model components from given data and formula | create_model.covs_and_comps |
| Create a model formula | create_model.formula |
| Parse the response variable and its likelihood model | create_model.likelihood |
| Parse the given modeling options | create_model.options |
| Parse given prior | create_model.prior |
| Helper function for plots | create_plot_df |
| Create a standardizing transform | create_scaling |
| Density and quantile functions of the inverse gamma distribution | dinvgamma_stanlike qinvgamma_stanlike |
| Draw pseudo-observations from posterior or prior predictive distribution | draw_pred |
| Quick way to create an example lgpfit, useful for debugging | example_fit |
| Print a fit summary. | fit_summary |
| An S4 class to represent analytically computed predictive distributions (conditional on hyperparameters) of an additive GP model | component_names,GaussianPrediction-method GaussianPrediction GaussianPrediction-class num_components,GaussianPrediction-method num_evalpoints,GaussianPrediction-method num_paramsets,GaussianPrediction-method show,GaussianPrediction-method |
| Extract parameter draws from lgpfit or stanfit | get_draws |
| Extract model predictions and function posteriors | get_pred |
| Compute a kernel matrix (covariance matrix) | kernel kernel_beta kernel_bin kernel_cat kernel_eq kernel_ns kernel_varmask kernel_zerosum |
| An S4 class to represent input for kernel matrix computations | component_names,KernelComputer-method KernelComputer KernelComputer-class num_components,KernelComputer-method num_evalpoints,KernelComputer-method num_paramsets,KernelComputer-method show,KernelComputer-method |
| Main function of the 'lgpr' package | lgp |
| An S4 class to represent an lgp expression | lgpexpr lgpexpr-class |
| An S4 class to represent the output of the 'lgp' function | clear_postproc,lgpfit-method component_names,lgpfit-method contains_postproc,lgpfit-method get_model,lgpfit-method get_stanfit,lgpfit-method is_f_sampled,lgpfit-method lgpfit lgpfit-class num_components,lgpfit-method plot,lgpfit,missing-method postproc,lgpfit-method show,lgpfit-method |
| An S4 class to represent an lgp formula | lgpformula lgpformula-class |
| An S4 class to represent an additive GP model | component_info,lgpmodel-method component_names,lgpmodel-method covariate_info,lgpmodel-method is_f_sampled,lgpmodel-method lgpmodel lgpmodel-class num_components,lgpmodel-method parameter_info,lgpmodel-method show,lgpmodel-method |
| An S4 class to represent the right-hand side of an lgp formula | lgprhs lgprhs-class |
| An S4 class to represent variable scaling | lgpscaling lgpscaling-class |
| An S4 class to represent a data set simulated using the additive GP formalism | lgpsim lgpsim-class plot,lgpsim,missing-method show,lgpsim-method |
| An S4 class to represent one formula term | lgpterm lgpterm-class |
| Print a model summary. | model_summary param_summary |
| Create test input points for prediction | new_x |
| Operations on formula terms and expressions | *,lgpterm,lgpterm-method +,lgprhs,lgprhs-method +,lgprhs,lgpterm-method +,lgpterm,lgpterm-method operations |
| Plot a generated/fit model component | plot_api_c |
| Plot longitudinal data and/or model fit so that each subject/group has their own panel | plot_api_g |
| Visualize all model components | plot_components |
| Vizualizing longitudinal data | plot_data |
| Visualize the distribution of parameter draws | plot_beta plot_draws plot_effect_times plot_warp |
| Visualize input warping function with several steepness parameter values | plot_inputwarp |
| Plot the inverse gamma-distribution pdf | plot_invgamma |
| Visualizing model predictions or inferred covariate effects | plot_f plot_pred |
| Visualize an lgpsim object (simulated data) | plot_sim |
| Graphical posterior predictive checks | ppc |
| Posterior predictions and function posteriors | pred |
| An S4 class to represent prior or posterior draws from an additive function distribution. | component_names,Prediction-method num_components,Prediction-method num_evalpoints,Prediction-method num_paramsets,Prediction-method Prediction Prediction-class show,Prediction-method |
| Prior (predictive) sampling | prior_pred sample_param_prior |
| Convert given prior to numeric format | prior_to_num |
| Prior definitions | bet gam gam, igam igam, log_normal log_normal, normal normal, priors student_t student_t, uniform uniform, |
| Function for reading the built-in proteomics data | read_proteomics_data |
| Assess component relevances | relevances |
| S4 generics for lgpfit, lgpmodel, and other objects | clear_postproc component_info component_names contains_postproc covariate_info get_model get_stanfit is_f_sampled num_components num_evalpoints num_paramsets parameter_info postproc s4_generics |
| Fitting a model | optimize_model sample_model |
| Select relevant components | select select.integrate select_freq select_freq.integrate |
| Printing formula object info using the show generic | show show,lgpformula-method show,lgprhs-method show,lgpterm-method |
| Simulate latent function components for longitudinal data analysis | sim.create_f |
| Create an input data frame X for simulated data | sim.create_x |
| Simulate noisy observations | sim.create_y |
| Compute all kernel matrices when simulating data | sim.kernels |
| Generate an artificial longitudinal data set | simulate_data |
| Split data into training and test sets | split split_by_factor split_data split_random split_within_factor split_within_factor_random |
| A very small artificial test data, used mostly for unit tests | testdata_001 |
| Medium-size artificial test data, used mostly for tutorials | testdata_002 |
| Validate S4 class objects | validate validate_GaussianPrediction validate_lgpexpr validate_lgpfit validate_lgpformula validate_lgpscaling validate_Prediction |
| Variance masking function | var_mask |
| Input warping function | warp_input |
