Package index
-
as_epidist_linelist_data()
- Create an epidist_linelist_data object
-
as_epidist_linelist_data(<data.frame>)
- Create an epidist_linelist_data object from a data frame with event dates
-
as_epidist_linelist_data(<default>)
- Create an epidist_linelist_data object from vectors of event times
-
assert_epidist(<epidist_linelist_data>)
- Assert validity of
epidist_linelist_data
objects
-
is_epidist_linelist_data()
- Check if data has the
epidist_linelist_data
class
-
new_epidist_linelist_data()
- Class constructor for
epidist_linelist_data
objects
-
epidist()
- Fit epidemiological delay distributions using a
brms
interface
-
epidist(<default>)
- Default method used for interface using
brms
-
as_epidist_naive_model()
- Prepare naive model to pass through to
brms
-
as_epidist_naive_model(<epidist_linelist_data>)
- The naive model method for
epidist_linelist_data
objects
-
is_epidist_naive_model()
- Check if data has the
epidist_naive_model
class
-
new_epidist_naive_model()
- Class constructor for
epidist_naive_model
objects
-
as_epidist_latent_model()
- Convert an object to an
epidist_latent_model
object
-
as_epidist_latent_model(<epidist_linelist_data>)
- The latent model method for
epidist_linelist_data
objects
-
epidist_family_model(<epidist_latent_model>)
- Create the model-specific component of an
epidist
custom family
-
epidist_formula_model(<epidist_latent_model>)
- Define the model-specific component of an
epidist
custom formula
-
epidist_gen_log_lik_latent()
- Create a function to calculate the pointwise log likelihood of the latent model
-
is_epidist_latent_model()
- Check if data has the
epidist_latent_model
class
-
new_epidist_latent_model()
- Class constructor for
epidist_latent_model
objects
-
add_mean_sd()
- Add natural scale mean and standard deviation parameters
-
add_mean_sd(<default>)
- Default method for add natural scale parameters
-
add_mean_sd(<gamma_samples>)
- Add natural scale mean and standard deviation parameters for a latent gamma model
-
add_mean_sd(<lognormal_samples>)
- Add natural scale mean and standard deviation parameters for a latent lognormal model
-
predict_delay_parameters()
predict_dpar()
- Extract samples of the delay distribution parameters
-
epidist_diagnostics()
- Diagnostics for
epidist_fit
models
-
assert_epidist()
- Validation for epidist objects
-
epidist_family()
- Define
epidist
family
-
epidist_family_model()
epidist_formula_model()
- The model-specific parts of an
epidist_family()
call
-
epidist_family_model(<default>)
- Default method for defining a model specific family
-
epidist_family_reparam()
- Reparameterise an
epidist
family to alignbrms
and Stan
-
epidist_family_reparam(<default>)
- Default method for families which do not require a reparameterisation
-
epidist_family_reparam(<gamma>)
- Reparameterisation for the gamma family
-
epidist_family_model()
epidist_formula_model()
- The model-specific parts of an
epidist_family()
call
-
epidist_formula()
- Define a model specific formula
-
epidist_formula_model(<default>)
- Default method for defining a model specific formula
-
epidist_family_prior()
- Family specific prior distributions
-
epidist_family_prior(<default>)
- Default family specific prior distributions
-
epidist_family_prior(<lognormal>)
- Family specific prior distributions for the lognormal family
-
epidist_model_prior()
- Model specific prior distributions
-
epidist_model_prior(<default>)
- Default model specific prior distributions
-
epidist_prior()
- Define prior distributions using
brms
defaults, model specific priors, family specific priors, and user provided priors
-
epidist_stancode()
- Define model specific Stan code
-
epidist_stancode(<default>)
- Default method for defining model specific Stan code
-
epidist_gen_posterior_epred()
- Create a function to draw from the expected value of the posterior predictive distribution for a latent model
-
epidist_gen_posterior_predict()
- Create a function to draw from the posterior predictive distribution for a latent model
-
simulate_exponential_cases()
- Simulate exponential cases
-
simulate_gillespie()
- Simulate cases from a stochastic SIR model
-
simulate_secondary()
- Simulate secondary events based on a delay distribution
-
simulate_uniform_cases()
- Simulate cases from a uniform distribution
-
sierra_leone_ebola_data
- Ebola linelist data from Fang et al. (2016)