| Add natural scale mean and standard deviation parameters | add_mean_sd |
| Default method for add natural scale parameters | add_mean_sd.default |
| Add natural scale mean and standard deviation parameters for a latent gamma model | add_mean_sd.gamma_samples |
| Add natural scale mean and standard deviation parameters for a latent lognormal model | add_mean_sd.lognormal_samples |
| Create an epidist_aggregate_data object | as_epidist_aggregate_data |
| Create an epidist_aggregate_data object from a data.frame | as_epidist_aggregate_data.data.frame |
| Create an epidist_aggregate_data object from vectors of event times | as_epidist_aggregate_data.default |
| Convert linelist data to aggregate format | as_epidist_aggregate_data.epidist_linelist_data |
| Convert an object to an 'epidist_latent_model' object | as_epidist_latent_model |
| The latent model method for 'epidist_aggregate_data' objects | as_epidist_latent_model.epidist_aggregate_data |
| The latent model method for 'epidist_linelist_data' objects | as_epidist_latent_model.epidist_linelist_data |
| Create an epidist_linelist_data object | as_epidist_linelist_data |
| Create an epidist_linelist_data object from a data frame with event dates | as_epidist_linelist_data.data.frame |
| Create an epidist_linelist_data object from vectors of event times | as_epidist_linelist_data.default |
| Convert aggregate data to linelist format | as_epidist_linelist_data.epidist_aggregate_data |
| Convert an object to an 'epidist_marginal_model' object | as_epidist_marginal_model |
| The marginal model method for 'epidist_aggregate_data' objects | as_epidist_marginal_model.epidist_aggregate_data |
| The marginal model method for 'epidist_linelist_data' objects | as_epidist_marginal_model.epidist_linelist_data |
| Convert an object to an 'epidist_naive_model' object | as_epidist_naive_model |
| The naive model method for 'epidist_aggregate_data' objects | as_epidist_naive_model.epidist_aggregate_data |
| The naive model method for 'epidist_linelist_data' objects | as_epidist_naive_model.epidist_linelist_data |
| Validation for epidist objects | assert_epidist |
| Assert validity of 'epidist_aggregate_data' objects | assert_epidist.epidist_aggregate_data |
| Assert validity of 'epidist_linelist_data' objects | assert_epidist.epidist_linelist_data |
| Fit epidemiological delay distributions using a 'brms' interface | epidist |
| Diagnostics for 'epidist_fit' models | epidist_diagnostics |
| Define 'epidist' family | epidist_family |
| The model-specific parts of an 'epidist_family()' call | epidist_family_model epidist_formula_model |
| Default method for defining a model specific family | epidist_family_model.default |
| Create the model-specific component of an 'epidist' custom family | epidist_family_model.epidist_latent_model |
| Create the model-specific component of an 'epidist' custom family | epidist_family_model.epidist_marginal_model |
| Reparameterise an 'epidist' family to align 'brms' and Stan | epidist_family_param |
| Default method for families which do not require a reparameterisation | epidist_family_param.default |
| Family specific prior distributions | epidist_family_prior |
| Default family specific prior distributions | epidist_family_prior.default |
| Family specific prior distributions for the lognormal family | epidist_family_prior.lognormal |
| Define a model specific formula | epidist_formula |
| Default method for defining a model specific formula | epidist_formula_model.default |
| Define the model-specific component of an 'epidist' custom formula for the latent model | epidist_formula_model.epidist_latent_model |
| Define the model-specific component of an 'epidist' custom formula for the marginal model | epidist_formula_model.epidist_marginal_model |
| Define the model-specific component of an 'epidist' custom formula for the naive model | epidist_formula_model.epidist_naive_model |
| Create a function to calculate the marginalised log likelihood for double censored and truncated delay distributions | epidist_gen_log_lik |
| Create a function to draw from the expected value of the posterior predictive distribution for a model | epidist_gen_posterior_epred |
| Create a function to draw from the posterior predictive distribution for a double censored and truncated delay distribution | epidist_gen_posterior_predict |
| Model specific prior distributions | epidist_model_prior |
| Default model specific prior distributions | epidist_model_prior.default |
| Model specific prior distributions for latent models | epidist_model_prior.epidist_latent_model |
| Define custom prior distributions for epidist models | epidist_prior |
| Define model specific Stan code | epidist_stancode |
| Default method for defining model specific Stan code | epidist_stancode.default |
| Transform data for an epidist model | epidist_transform_data |
| The model-specific parts of an 'epidist_transform_data()' call | epidist_transform_data_model |
| Default method for transforming data for a model | epidist_transform_data_model.default |
| Transform data for the marginal model | epidist_transform_data_model.epidist_marginal_model |
| Transform data for the naive model | epidist_transform_data_model.epidist_naive_model |
| Check if data has the 'epidist_aggregate_data' class | is_epidist_aggregate_data |
| Check if data has the 'epidist_latent_model' class | is_epidist_latent_model |
| Check if data has the 'epidist_linelist_data' class | is_epidist_linelist_data |
| Check if data has the 'epidist_marginal_model' class | is_epidist_marginal_model |
| Check if data has the 'epidist_naive_model' class | is_epidist_naive_model |
| Class constructor for 'epidist_aggregate_data' objects | new_epidist_aggregate_data |
| Class constructor for 'epidist_latent_model' objects | new_epidist_latent_model |
| Class constructor for 'epidist_linelist_data' objects | new_epidist_linelist_data |
| Class constructor for 'epidist_marginal_model' objects | new_epidist_marginal_model |
| Class constructor for 'epidist_naive_model' objects | new_epidist_naive_model |
| Extract samples of the delay distribution parameters | predict_delay_parameters predict_dpar |
| Ebola linelist data from Fang et al. (2016) | sierra_leone_ebola_data |
| Simulate exponential cases | simulate_exponential_cases |
| Simulate cases from a stochastic SIR model | simulate_gillespie |
| Simulate secondary events based on a delay distribution | simulate_secondary |
| Simulate cases from a uniform distribution | simulate_uniform_cases |
