Function reference • epinowcast

Nowcast

Functions for nowcasting.

epinowcast(): Nowcast using partially observed data

plot(<epinowcast>): Plot method for epinowcast

summary(<epinowcast>): Summary method for epinowcast

Data converters

Functions for converting between data formats

enw_add_cumulative(): Calculate cumulative reported cases from incidence of new reports

enw_add_incidence(): Calculate incidence of new reports from cumulative reports

enw_aggregate_cumulative(): Aggregate observations over a given timestep for both report and reference dates.

enw_incidence_to_linelist(): Convert Aggregate Counts (Incidence) to a Line List

enw_linelist_to_incidence(): Convert a Line List to Aggregate Counts (Incidence)

Preprocess

Functions for preprocessing observations.

enw_add_delay(): Add a delay variable to the observations

enw_add_max_reported(): Add the maximum number of reported cases for each reference_date

enw_add_metaobs_features(): Add common metadata variables

enw_assign_group(): Assign a group to each row of a data.table

enw_complete_dates(): Complete missing reference and report dates

enw_construct_data(): Construct preprocessed data

enw_extend_date(): Extend a time series with additional dates

enw_filter_reference_dates(): Filter by reference dates

enw_filter_report_dates(): Filter by report dates

enw_flag_observed_observations(): Flag observed observations

enw_impute_na_observations(): Impute NA observations

enw_latest_data(): Filter observations to the latest available reported

enw_metadata(): Extract metadata from raw data

enw_metadata_delay(): Calculate reporting delay metadata for a given maximum delay

enw_missing_reference(): Extract reports with missing reference dates

enw_preprocess_data(): Preprocess observations

enw_reporting_triangle(): Construct the reporting triangle

enw_reporting_triangle_to_long(): Recast the reporting triangle from wide to long format

Model modules

Modular model components.

enw_expectation(): Expectation model module

enw_fit_opts(): Format model fitting options for use with stan

enw_missing(): Missing reference data model module

enw_obs(): Setup observation model and data

enw_reference(): Reference date logit hazard reporting model module

enw_report(): Report date logit hazard reporting model module

Helpers for defining model modules

Functions that help with the setup of model modules and that may be specific to individual modules.

add_max_observed_delay(): Add maximum observed delay

add_pmfs(): Add probability mass functions

convolution_matrix(): Construct a convolution matrix

enw_reference_by_report(): Construct a lookup of references dates by report

enw_reps_with_complete_refs(): Identify report dates with complete (i.e up to the maximum delay) reference dates

extract_obs_metadata(): Extract observation metadata

extract_sparse_matrix(): Extract sparse matrix elements

latest_obs_as_matrix(): Convert latest observed data to a matrix

simulate_double_censored_pmf(): Simulate daily double censored PMF

Model tools

Generic functions that assist with combining and using modular models.

enw_formula_as_data_list(): Format formula data for use with stan

enw_get_cache(): Retrieve Stan cache location

enw_model(): Load and compile the nowcasting model

enw_priors_as_data_list(): Convert prior data.frame to list

enw_replace_priors(): Replace default priors with user specified priors

enw_sample(): Fit a CmdStan model using NUTS

enw_set_cache(): Set caching location for Stan models

enw_stan_to_r(): Expose epinowcast stan functions in R

enw_unset_cache(): Unset Stan cache location

remove_profiling(): Remove profiling statements from a character vector representing stan code

write_stan_files_no_profile(): Write copies of the .stan files of a Stan model and its #include files with all profiling statements removed.

Postprocess

Functions for postprocessing model output.

enw_add_latest_obs_to_nowcast(): Add latest observations to nowcast output

enw_nowcast_samples(): Extract posterior samples for the nowcast prediction

enw_nowcast_summary(): Summarise the posterior nowcast prediction

enw_posterior(): Summarise the posterior

enw_pp_summary(): Posterior predictive summary

enw_quantiles_to_long(): Convert summarised quantiles from wide to long format

enw_summarise_samples(): Summarise posterior samples

Plot

Functions for plotting postprocessed nowcast model output.

enw_plot_nowcast_quantiles(): Plot nowcast quantiles

enw_plot_obs(): Generic quantile plot

enw_plot_pp_quantiles(): Plot posterior prediction quantiles

enw_plot_quantiles(): Generic quantile plot

enw_plot_theme(): Package plot theme

plot(<epinowcast>): Plot method for epinowcast

Model validation

Functions for validating model fits.

enw_score_nowcast(): Evaluate nowcasts using proper scoring rules

Formula design tools

Functions that assist with interpreting model formulas

as_string_formula(): Converts formulas to strings

construct_re(): Constructs random effect terms

construct_rw(): Constructs random walk terms

enw_formula(): Define a model using a formula interface

enw_manual_formula(): Define a model manually using fixed and random effects

parse_formula(): Parse a formula into components

re(): Defines random effect terms using the lme4 syntax

remove_rw_terms(): Remove random walk terms from a formula object

rw(): Adds random walks with Gaussian steps to the model.

rw_terms(): Finds random walk terms in a formula object

split_formula_to_terms(): Split formula into individual terms

Model design tools

Functions that assist with designing models.

enw_add_cumulative_membership(): Add a cumulative membership effect to a data.frame

enw_add_pooling_effect(): Add a pooling effect to model design metadata

enw_design(): A helper function to construct a design matrix from a formula

enw_effects_metadata(): Extracts metadata from a design matrix

enw_one_hot_encode_feature(): One-hot encode a variable and column-bind it to the original data.table

Datasets

Package datasets used in examples and by users to explore the package functionality.

enw_example(): Load a package example

germany_covid19_hosp: Hospitalisations in Germany by date of report and reference

Simulate Datasets

Tools for simulating datasets

enw_simulate_missing_reference(): Simulate observations with a missing reference date.

Check inputs

Functions to check the structure of user inputs.

check_calendar_timestep(): Check calendar timestep

check_group(): Check observations for reserved grouping variables

check_group_date_unique(): Check observations for uniqueness of grouping variables with respect to reference_date and report_date

check_max_delay(): Check appropriateness of maximum delay

check_module(): Check a model module contains the required components

check_modules_compatible(): Check that model modules have compatible specifications

check_numeric_timestep(): Check Numeric Timestep

check_observation_indicator(): Check observation indicator

check_quantiles(): Check required quantiles are present

check_timestep(): Check timestep

check_timestep_by_date(): Check timestep by date

check_timestep_by_group(): Check timestep by group

Utilities

Utility functions

aggregate_rolling_sum(): Internal function to perform rolling sum aggregation

coerce_date(): Coerce Dates

coerce_dt(): Coerce data.tables

date_to_numeric_modulus(): Convert date column to numeric and calculate its modulus with given timestep.

get_internal_timestep(): Get internal timestep

is.Date(): Check an object is a Date

stan_fns_as_string(): Read in a stan function file as a character string