Package index • epidist

Linelist data

Functions for preparing linelist data

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

as_epidist_linelist_data(<epidist_aggregate_data>): Convert aggregate data to linelist format

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

Aggregate data

Functions for preparing aggregate data

as_epidist_aggregate_data(): Create an epidist_aggregate_data object

as_epidist_aggregate_data(<data.frame>): Create an epidist_aggregate_data object from a data.frame

as_epidist_aggregate_data(<default>): Create an epidist_aggregate_data object from vectors of event times

as_epidist_aggregate_data(<epidist_linelist_data>): Convert linelist data to aggregate format

assert_epidist(<epidist_aggregate_data>): Assert validity of epidist_aggregate_data objects

is_epidist_aggregate_data(): Check if data has the epidist_aggregate_data class

new_epidist_aggregate_data(): Class constructor for epidist_aggregate_data objects

Model fitting

Functions for fitting delay distribution models using brms

epidist(): Fit epidemiological delay distributions using a brms interface

Naive model

Specific methods for the naive model

as_epidist_naive_model(): Convert an object to an epidist_naive_model object

as_epidist_naive_model(<epidist_aggregate_data>): The naive model method for epidist_aggregate_data objects

as_epidist_naive_model(<epidist_linelist_data>): The naive model method for epidist_linelist_data objects

epidist_formula_model(<epidist_naive_model>): Define the model-specific component of an epidist custom formula for the naive model

epidist_transform_data_model(<epidist_naive_model>): Transform data for the naive model

is_epidist_naive_model(): Check if data has the epidist_naive_model class

new_epidist_naive_model(): Class constructor for epidist_naive_model objects

Latent model

Specific methods for the latent model

as_epidist_latent_model(): Convert an object to an epidist_latent_model object

as_epidist_latent_model(<epidist_aggregate_data>): The latent model method for epidist_aggregate_data objects

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 for the latent model

epidist_model_prior(<epidist_latent_model>): Model specific prior distributions for latent models

is_epidist_latent_model(): Check if data has the epidist_latent_model class

new_epidist_latent_model(): Class constructor for epidist_latent_model objects

Marginal model

Specific methods for the marginal model

as_epidist_marginal_model(): Convert an object to an epidist_marginal_model object

as_epidist_marginal_model(<epidist_aggregate_data>): The marginal model method for epidist_aggregate_data objects

as_epidist_marginal_model(<epidist_linelist_data>): The marginal model method for epidist_linelist_data objects

epidist_family_model(<epidist_marginal_model>): Create the model-specific component of an epidist custom family

epidist_formula_model(<epidist_marginal_model>): Define the model-specific component of an epidist custom formula for the marginal model

epidist_transform_data_model(<epidist_marginal_model>): Transform data for the marginal model

is_epidist_marginal_model(): Check if data has the epidist_marginal_model class

new_epidist_marginal_model(): Class constructor for epidist_marginal_model objects

Postprocess

Functions for postprocessing model output

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

Diagnostic functions

epidist_diagnostics(): Diagnostics for epidist_fit models

Assert validity of objects

Functions used to assert the validity of package objects

assert_epidist(): Validation for epidist objects

Family

Functions related to specifying custom brms families

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_param(): Reparameterise an epidist family to align brms and Stan

epidist_family_param(<default>): Default method for families which do not require a reparameterisation

Formula

Functions related to specifying custom brms formula

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

Transform data

Transform data using the formula and family information

epidist_transform_data(): Transform data for an epidist model

epidist_transform_data_model(): The model-specific parts of an epidist_transform_data() call

epidist_transform_data_model(<default>): Default method for transforming data for a model

Prior distributions

Functions for specifying prior distributions

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 custom prior distributions for epidist models

Stan code

Functions for specifying custom Stan code to put into brms

epidist_stancode(): Define model specific Stan code

epidist_stancode(<default>): Default method for defining model specific Stan code

Generator model functions

Generator functions for creating family specific model utilities.

epidist_gen_log_lik(): Create a function to calculate the marginalised log likelihood for double censored and truncated delay distributions

epidist_gen_posterior_epred(): Create a function to draw from the expected value of the posterior predictive distribution for a model

epidist_gen_posterior_predict(): Create a function to draw from the posterior predictive distribution for a double censored and truncated delay distribution

Simulation

Tools for simulating datasets

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

Data

Data included with the package

sierra_leone_ebola_data: Ebola linelist data from Fang et al. (2016)

Reexported functions

reexports lognormal weibull Gamma bf: Objects exported from other packages