Provides a user friendly interface around package functionality to produce a nowcast from observed preprocessed data, and a series of user defined models. By default a model that assumes a fixed parametric reporting distribution with a flexible expectation model is used. Explore the individual model components for additional documentation and see the package case studies for example model specifications for different tasks.

## Usage

```
epinowcast(
data,
reference = epinowcast::enw_reference(parametric = ~1, distribution = "lognormal",
non_parametric = ~0, data = data),
report = epinowcast::enw_report(non_parametric = ~0, structural = ~0, data = data),
expectation = epinowcast::enw_expectation(r = ~0 + (1 | day:.group), generation_time =
1, observation = ~1, latent_reporting_delay = 1, data = data),
missing = epinowcast::enw_missing(formula = ~0, data = data),
obs = epinowcast::enw_obs(family = "negbin", data = data),
fit = epinowcast::enw_fit_opts(sampler = epinowcast::enw_sample, nowcast = TRUE, pp =
FALSE, likelihood = TRUE, debug = FALSE, output_loglik = FALSE),
model = epinowcast::enw_model(),
priors,
...
)
```

## Arguments

- data
Output from

`enw_preprocess_data()`

.- reference
The reference date indexed reporting process model specification as defined using

`enw_reference()`

.- report
The report date indexed reporting process model specification as defined using

`enw_report()`

.- expectation
The expectation model specification as defined using

`enw_expectation()`

. By default this is set to be a highly flexible random effect by reference date for each group and thus weakly informed. Depending on your context (and in particular the density of data reporting) other choices that enforce more assumptions may be more appropriate (for example a weekly random walk (specified using`rw(week, by = .group)`

)).- missing
The missing reference date model specification as defined using

`enw_missing()`

. By default this is set to not be used.- obs
The observation model as defined by

`enw_obs()`

. Observations are also processed within this function for use in modelling.- fit
Model fit options as defined using

`enw_fit_opts()`

. This includes the sampler function to use (with the package default being`enw_sample()`

), whether or now a nowcast should be used, etc. See`enw_fit_opts()`

for further details.- model
The model to use within

`fit`

. By default this uses`enw_model()`

.- priors
A

`data.frame`

with the following variables:`variable`

,`mean`

,`sd`

describing normal priors. Priors in the appropriate format are returned by`enw_reference()`

as well as by other similar model specification functions. Priors in this data.frame replace the default priors specified by each model component.- ...
Additional model modules to pass to

`model`

. User modules may be used but currently require the supplied`model`

to be adapted.

## Value

A object of the class "epinowcast" which inherits from
`enw_preprocess_data()`

and `data.table`

, and combines the output from
the sampler specified in `enw_fit_opts()`

.

## See also

Other epinowcast:
`plot.epinowcast()`

,
`summary.epinowcast()`

## Examples

```
if (FALSE) { # interactive()
# Load data.table and ggplot2
library(data.table)
library(ggplot2)
# Use 2 cores
options(mc.cores = 2)
# Load and filter germany hospitalisations
nat_germany_hosp <-
germany_covid19_hosp[location == "DE"][age_group %in% "00+"]
nat_germany_hosp <- enw_filter_report_dates(
nat_germany_hosp,
latest_date = "2021-10-01"
)
# Make sure observations are complete
nat_germany_hosp <- enw_complete_dates(
nat_germany_hosp,
by = c("location", "age_group")
)
# Make a retrospective dataset
retro_nat_germany <- enw_filter_report_dates(
nat_germany_hosp,
remove_days = 40
)
retro_nat_germany <- enw_filter_reference_dates(
retro_nat_germany,
include_days = 40
)
# Get latest observations for the same time period
latest_obs <- enw_latest_data(nat_germany_hosp)
latest_obs <- enw_filter_reference_dates(
latest_obs,
remove_days = 40, include_days = 20
)
# Preprocess observations (note this maximum delay is likely too short)
pobs <- enw_preprocess_data(retro_nat_germany, max_delay = 20)
# Fit the default nowcast model and produce a nowcast
# Note that we have reduced samples for this example to reduce runtimes
nowcast <- epinowcast(pobs,
fit = enw_fit_opts(
save_warmup = FALSE, pp = TRUE,
chains = 2, iter_warmup = 500, iter_sampling = 500
)
)
nowcast
# plot the nowcast vs latest available observations
plot(nowcast, latest_obs = latest_obs)
# plot posterior predictions for the delay distribution by date
plot(nowcast, type = "posterior") +
facet_wrap(vars(reference_date), scale = "free")
}
```