Package 'nfidd'

Simulate symptom onset and hospitalization times from infection times

Description

Simulate symptom onset and hospitalization times from infection times

Usage

add_delays(infection_times)

Arguments

infection_times

A data frame containing a column of infection times

Value

A data frame with columns for infection time, onset time, and hospitalization time (with 70

Examples

delayed_infections <- add_delays(infection_times)
head(delayed_infections)

---

Discretise a Continuous Delay Distribution

Description

This function discretises a continuous delay distribution into a probability mass function (PMF) over discrete days.

Usage

censored_delay_pmf(rgen, max, n = 1e+06, ...)

Arguments

rgen	A function that generates random delays, e.g., 'rgamma', 'rlnorm'.
max	The maximum delay.
n	The number of replicates to simulate. Defaults to '1e+6'.
...	Additional parameters of the delay distribution.

Value

A vector of probabilities corresponding to discrete indices from '0' to 'max', representing the discretised delay distribution.

Examples

censored_delay_pmf(rgen = rgamma, max = 14, shape = 5, rate = 1)

---

Convolve a Time Series with a Delay Distribution

Description

This function convolves a time series with a delay distribution given as a probability mass function (PMF).

Usage

convolve_with_delay(ts, delay_pmf)

Arguments

ts	A vector of the time series to convolve.
delay_pmf	The probability mass function of the delay, given as a vector of probabilities, corresponding to discrete indices 0, 1, 2 of the discretised delay distribution.

Value

A vector of the convolved time series.

Examples

convolve_with_delay(ts = c(10, 14, 10, 10), delay_pmf = c(0.1, 0.6, 0.3))

---

Geometric Differenced Autoregressive Process

Description

This function generates a geometric differenced autoregressive process.

Usage

geometric_diff_ar(init, noise, std, damp)

Arguments

init	The initial value.
noise	A vector of steps (on the standard normal scale).
std	The step size of the random walk.
damp	A damping parameter.

Value

A vector of the generated geometric differenced autoregressive process.

Examples

geometric_diff_ar(init = 1, noise = rnorm(100), std = 0.1, damp = 0.1)

---

Geometric Random Walk

Description

This function generates a geometric random walk.

Usage

geometric_random_walk(init, noise, std)

Arguments

init	The initial value.
noise	A vector of steps (on the standard normal scale).
std	The step size of the random walk.

Value

A vector of the generated geometric random walk.

Examples

geometric_random_walk(init = 1, noise = rnorm(100), std = 0.1)

---

Infection times

Description

A dataset containing random infection times from a branching process model, generated using the code in data-raw/epicurve.r

Usage

infection_times

Format

A data frame with a single column

infection_time

the times at which individuals were infected (and became infectious)

---

Convert infection times to a daily time series

Description

Convert infection times to a daily time series

Usage

make_daily_infections(infection_times)

Arguments

infection_times

A data frame containing a column of infection times

Value

A data frame with columns infection_day and infections, containing the daily count of infections

Examples

make_daily_infections(infection_times)

---

Generate a probability mass function for the generation time

Description

Generate a probability mass function for the generation time

Usage

make_gen_time_pmf(max = 14, shape = 4, rate = 1)

Arguments

max	Maximum delay to consider
shape	Shape parameter for the gamma distribution
rate	Rate parameter for the gamma distribution

Value

A vector of probabilities representing the generation time PMF

---

Generate a probability mass function for the incubation period

Description

Generate a probability mass function for the incubation period

Usage

make_ip_pmf(max = 14, shape = 5, rate = 1)

Arguments

max	Maximum delay to consider
shape	Shape parameter for the gamma distribution
rate	Rate parameter for the gamma distribution

Value

A vector of probabilities representing the incubation period PMF

---

Forecasts from a mechanistic model

Description

A dataset containing forecasts from a mechanistic model, generated using the code in data-raw/generate-example-forecasts.r

Usage

mech_forecasts

Format

A [tibble::tibble()] with a 7 columns

day

the day for which the forecast was made

.draw

an ID of the posterior predictive sample

.variable

name of the variable

.value

predicted value

.horizon

the forecast horizon in days

target_day

the day on which the forecast was made (using data up to this day)

model

the name of the model

---

Create a CmdStanModel with NFIDD Stan functions

Description

This function creates a CmdStanModel object using a specified Stan model from the NFIDD package and optionally includes additional user-specified Stan files.

Usage

nfidd_cmdstan_model(model_name, include_paths = nfidd::nfidd_stan_path(), ...)

Arguments

model_name	Character string specifying which Stan model to use.
include_paths	Character vector of paths to include for Stan compilation. Defaults to the result of 'nfidd_stan_path()'.
...	Additional arguments passed to cmdstanr::cmdstan_model().

Value

A CmdStanModel object.

Examples

if (!is.null(cmdstanr::cmdstan_version(error_on_NA = FALSE))) {
  model <- nfidd_cmdstan_model("simple-nowcast", compile = FALSE)
  model
}

---

Load Stan functions as a string

Description

Load Stan functions as a string

Usage

nfidd_load_stan_functions(
  functions = NULL,
  stan_path = nfidd::nfidd_stan_path(),
  wrap_in_block = FALSE,
  write_to_file = FALSE,
  output_file = "nfidd_functions.stan"
)

Arguments

functions	Character vector of function names to load. Defaults to all functions.
stan_path	Character string, the path to the Stan code. Defaults to the path to the Stan code in the nfidd package.
wrap_in_block	Logical, whether to wrap the functions in a 'functions' block. Default is FALSE.
write_to_file	Logical, whether to write the output to a file. Default is FALSE.
output_file	Character string, the path to write the output file if write_to_file is TRUE. Defaults to "nfidd_functions.stan".

Value

A character string containing the requested Stan functions

See Also

Other stantools: nfidd_stan_function_files(), nfidd_stan_functions(), nfidd_stan_path()

---

Get Stan files containing specified functions

Description

This function retrieves Stan files from a specified directory, optionally filtering for files that contain specific functions.

Usage

nfidd_stan_function_files(
  functions = NULL,
  stan_path = nfidd::nfidd_stan_path()
)

Arguments

functions	Character vector of function names to search for. If NULL, all Stan files are returned.
stan_path	Character string specifying the path to the directory containing Stan files. Defaults to the Stan path of the nfidd package.

Value

A character vector of file paths to Stan files.

See Also

Other stantools: nfidd_load_stan_functions(), nfidd_stan_functions(), nfidd_stan_path()

---

Get Stan function names from Stan files

Description

This function reads all Stan files in the specified directory and extracts the names of all functions defined in those files.

Usage

nfidd_stan_functions(stan_path = nfidd::nfidd_stan_path())

Arguments

stan_path

Character string specifying the path to the directory containing Stan files. Defaults to the Stan path of the nfidd package.

Value

A character vector containing unique names of all functions found in the Stan files.

See Also

Other stantools: nfidd_load_stan_functions(), nfidd_stan_function_files(), nfidd_stan_path()

---

List Available Stan Models in NFIDD

Description

This function finds all available Stan models in the NFIDD package and returns their names without the .stan extension.

Usage

nfidd_stan_models(stan_path = nfidd::nfidd_stan_path())

Arguments

stan_path

Character string specifying the path to Stan files. Defaults to the result of 'nfidd_stan_path()'.

Value

A character vector of available Stan model names.

Examples

nfidd_stan_models()

---

Get the path to Stan code

Description

Get the path to Stan code

Usage

nfidd_stan_path()

Value

A character string with the path to the Stan code

See Also

Other stantools: nfidd_load_stan_functions(), nfidd_stan_function_files(), nfidd_stan_functions()

---

Simulate Infections using the Renewal Equation

Description

This function simulates infections using the renewal equation.

Usage

renewal(I0, R, gen_time)

Arguments

I0	The initial number of infections.
R	The reproduction number, given as a vector with one entry per time point.
gen_time	The generation time distribution, given as a vector with one entry per day after infection (the first element corresponding to one day after infection).

Value

A vector of simulated infections over time.

Examples

renewal(
  I0 = 5,
  R = c(rep(3, 4), rep(0.5, 5)),
  gen_time = c(0.1, 0.2, 0.3, 0.2, 0.1)
)

---

Forecasts from a semi-mechanistic model

Description

A dataset containing forecasts from a semi-mechanistic model (using a geometric random walk prior on the reproduction number), generated using the code in data-raw/generate-example-forecasts.r

Usage

rw_forecasts

Format

A [tibble::tibble()] with a 7 columns

day

the day for which the forecast was made

.draw

an ID of the posterior predictive sample

.variable

name of the variable

.value

predicted value

.horizon

the forecast horizon in days

target_day

the day on which the forecast was made (using data up to this day)

model

the name of the model

---

Simulate symptom onsets from daily infection counts

Description

Simulate symptom onsets from daily infection counts

Usage

simulate_onsets(
  inf_ts,
  gen_time_pmf = make_gen_time_pmf(),
  ip_pmf = make_ip_pmf()
)

Arguments

inf_ts	A data frame containing columns infection_day and infections, as returned by 'make_daily_infections()'.
gen_time_pmf	A vector of probabilities representing the generation time PMF, as returned by 'make_gen_time_pmf()'.
ip_pmf	A vector of probabilities representing the incubation period PMF, as returned by 'make_ip_pmf()'.

Value

A data frame with columns day, onsets, and infections containing the daily count of symptom onsets and infections

Examples

gt_pmf <- make_gen_time_pmf()
ip_pmf <- make_ip_pmf()
simulate_onsets(make_daily_infections(infection_times), gt_pmf, ip_pmf)

---

Forecasts from a semi-mechanistic model with additional statistical complexity

Description

A dataset containing forecasts from a semi-mechanistic model (using an autoregressive prior for reproduction number), generated using the code in data-raw/generate-example-forecasts.r

Usage

stat_forecasts

Format

A [tibble::tibble()] with a 7 columns

day

the day for which the forecast was made

.draw

an ID of the posterior predictive sample

.variable

name of the variable

.value

predicted value

.horizon

the forecast horizon in days

target_day

the day on which the forecast was made (using data up to this day)

model

the name of the model

---