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