Hyperlink functions and use consistent styling

R/adjust.R

@@ -3,12 +3,12 @@
 #' @description  `r lifecycle::badge("stable")`
 #' Maps from cases by date of infection to date of report via date of
 #' onset.
-#' @param infections `data.table` containing a `date` variable and a numeric
+#' @param infections `<data.table>` containing a `date` variable and a numeric
 #' `cases` variable.
 #'
 #' @param delay_defs A list of single row data.tables that each  defines a
 #' delay distribution (model, parameters and maximum delay for each model).
-#' See `lognorm_dist_def` for an example of the structure.
+#' See [lognorm_dist_def()] for an example of the structure.
 #'
 #' @param reporting_effect A numeric vector of length 7 that allows the scaling
 #' of reported cases by the day on which they report (1 = Monday, 7 = Sunday).

R/create.R

@@ -74,8 +74,8 @@ create_clean_reported_cases <- function(reported_cases, horizon,
 #' This functions creates a data frame of reported cases that has been smoothed
 #' using a centred partial rolling average (with a period set by
 #' `smoothing_window`) and shifted back in time by some delay. It is used by
-#' `estimate_infections` to generate the mean shifted prior on which the back
-#' calculation method (see `backcalc_opts()`) is based.
+#' [estimate_infections()] to generate the mean shifted prior on which the back
+#' calculation method (see [backcalc_opts()]) is based.
 #'
 #' @param smoothing_window Numeric, the rolling average smoothing window
 #' to apply. Must be odd in order to be defined as a centred average.
@@ -87,7 +87,7 @@ create_clean_reported_cases <- function(reported_cases, horizon,
 #' @importFrom data.table copy shift frollmean fifelse .N
 #' @importFrom stats lm
 #' @importFrom runner mean_run
-#' @return A data frame for shifted reported cases
+#' @return A `<data.frame>` for shifted reported cases
 #' @export
 #' @author Sam Abbott
 #' @examples
@@ -144,8 +144,8 @@ create_shifted_cases <- function(reported_cases, shift,
 #' Construct the Required Future Rt assumption
 #'
 #' @description `r lifecycle::badge("stable")`
-#' Converts the `future` argument from `rt_opts()` into arguments that can be
-#' passed to `stan`.
+#' Converts the `future` argument from [rt_opts()] into arguments that can be
+#' passed to `{stan}`.
 #'
 #' @param future A character string or integer. This argument indicates how to
 #' set future Rt values. Supported options are to project using the Rt model
@@ -184,10 +184,10 @@ create_future_rt <- function(future = "latest", delay = 0) {
 #' Create Time-varying Reproduction Number Data
 #'
 #' @description `r lifecycle::badge("stable")`
-#' Takes the output from `rt_opts()` and converts it into a list understood by
-#' `stan`.
-#' @param rt A list of options as generated by `rt_opts()` defining Rt
-#' estimation. Defaults to `rt_opts()`. Set to `NULL` to switch to using back
+#' Takes the output from [rt_opts()] and converts it into a list understood by
+#' `{stan}`.
+#' @param rt A list of options as generated by [rt_opts()] defining Rt
+#' estimation. Defaults to [rt_opts()]. Set to `NULL` to switch to using back
 #' calculation rather than generating infections using Rt.
 #'
 #' @param breakpoints An integer vector (binary) indicating the location of
@@ -256,11 +256,11 @@ create_rt_data <- function(rt = rt_opts(), breakpoints = NULL,
 #' Create Back Calculation Data
 #'
 #' @description `r lifecycle::badge("stable")`
-#' Takes the output of `backcalc_opts()` and converts it into a list understood
-#' by `stan`.
+#' Takes the output of [backcalc_opts()] and converts it into a list understood
+#' by `{stan}`.
 #'
-#' @param backcalc A list of options as generated by `backcalc_opts()` to
-#' define the back calculation. Defaults to `backcalc_opts()`.
+#' @param backcalc A list of options as generated by [backcalc_opts()] to
+#' define the back calculation. Defaults to [backcalc_opts()].
 #'
 #' @seealso backcalc_opts
 #' @importFrom data.table fcase
@@ -284,15 +284,15 @@ create_backcalc_data <- function(backcalc = backcalc_opts()) {
 #' Create Gaussian Process Data
 #'
 #' @description `r lifecycle::badge("stable")`
-#' Takes the output of `gp_opts()` and converts it into a list understood by
-#' `stan`.
-#' @param gp A list of options as generated by `gp_opts()` to define the
-#' Gaussian process. Defaults to `gp_opts()`.Set to NULL to disable the
+#' Takes the output of [gp_opts()] and converts it into a list understood by
+#' `{stan}`.
+#' @param gp A list of options as generated by [gp_opts()] to define the
+#' Gaussian process. Defaults to [gp_opts()]. Set to `NULL` to disable the
 #' Gaussian process.
 #' @param data A list containing the following numeric values:
 #' `t`, `seeding_time`, `horizon`.
 #' @importFrom data.table fcase
-#' @seealso gp_opts
+#' @seealso [gp_opts()]
 #' @return A list of settings defining the Gaussian process
 #' @export
 #' @author Sam Abbott
@@ -356,13 +356,13 @@ create_gp_data <- function(gp = gp_opts(), data) {
 #' Create Observation Model Settings
 #'
 #' @description `r lifecycle::badge("stable")`
-#' Takes the output of `obs_opts()` and converts it into a list understood
-#' by `stan`.
-#' @param obs A list of options as generated by `obs_opts()` defining the
-#' observation model. Defaults to `obs_opts()`.
+#' Takes the output of [obs_opts()] and converts it into a list understood
+#' by `{stan}`.
+#' @param obs A list of options as generated by [obs_opts()] defining the
+#' observation model. Defaults to [obs_opts()].
 #' @param dates A vector of dates used to calculate the day of the week.
-#' @seealso obs_opts
-#' @return A list of settings ready to be passed to stan defining
+#' @seealso [obs_opts()]
+#' @return A list of settings ready to be passed to `{stan}` defining
 #' the Observation Model
 #' @export
 #' @author Sam Abbott
@@ -408,14 +408,15 @@ create_obs_model <- function(obs = obs_opts(), dates) {
 #' Create Stan Data Required for estimate_infections
 #'
 #' @description`r lifecycle::badge("stable")`
-#' Takes the output of `stan_opts()` and converts it into a list understood by
-#' `stan`. Internally calls the other `create_` family of functions to
-#' construct a single list for input into stan with all data required present.
+#' Takes the output of [stan_opts()] and converts it into a list understood by
+#' `{stan}`. Internally calls the other `create_` family of functions to
+#' construct a single list for input into `{stan}` with all data required
+#' present.
 #'
-#' @param shifted_cases A dataframe of delay shifted cases
+#' @param shifted_cases A `<data.frame>` of delay shifted cases
 #'
 #' @param seeding_time Integer; seeding time, usually obtained using
-#' `get_seeding_time()`
+#' [get_seeding_time()].
 #'
 #' @inheritParams create_gp_data
 #' @inheritParams create_obs_model
@@ -497,11 +498,11 @@ create_stan_data <- function(reported_cases, seeding_time,
 
 #' Create Initial Conditions Generating Function
 #' @description `r lifecycle::badge("stable")`
-#' Uses the output of `create_stan_data` to create a function which can be used
-#' to sample from the prior distributions (or as close as possible) for
-#' parameters. Used in order to initialise each `stan` chain within a range of
+#' Uses the output of [create_stan_data()] to create a function which can be
+#' used to sample from the prior distributions (or as close as possible) for
+#' parameters. Used in order to initialise each `{stan}` chain within a range of
 #' plausible values.
-#' @param data A list of data as produced by `create_stan_data.`
+#' @param data A list of data as produced by [create_stan_data()].
 #' @return An initial condition generating function
 #' @importFrom purrr map2_dbl
 #' @importFrom truncnorm rtruncnorm
@@ -598,26 +599,26 @@ create_initial_conditions <- function(data) {
 #' Create a List of Stan Arguments
 #'
 #' @description `r lifecycle::badge("stable")`
-#' Generates a list of arguments as required by `rstan::sampling` or
-#' `rstan::vb` by combining the required options, with data, and type of
+#' Generates a list of arguments as required by [rstan::sampling()] or
+#' [rstan::vb()] by combining the required options, with data, and type of
 #' initialisation. Initialisation defaults to random but it is expected that
-#' `create_initial_conditions` will be used.
+#' [create_initial_conditions()] will be used.
 #'
-#' @param stan A list of stan options as generated by `stan_opts()`. Defaults
-#' to `stan_opts()`. Can be used to override `data`, `init`, and `verbose`
+#' @param stan A list of stan options as generated by [stan_opts()]. Defaults
+#' to [stan_opts()]. Can be used to override `data`, `init`, and `verbose`
 #' settings if desired.
 #'
-#' @param data A list of stan data as created by `create_stan_data`
+#' @param data A list of `{stan}` data as created by [create_stan_data()]
 #'
-#' @param init Initial conditions passed to `rstan`. Defaults to "random" but
-#' can also be a function (as supplied by `create_intitial_conditions`).
+#' @param init Initial conditions passed to `{rstan}`. Defaults to "random" but
+#' can also be a function (as supplied by [create_intitial_conditions()]).
 #'
 #' @param verbose Logical, defaults to `FALSE`. Should verbose progress
 #' messages be returned.
 #'
 #' @importFrom utils modifyList
 #'
-#' @return A list of stan arguments
+#' @return A list of `{stan}` arguments
 #' @author Sam Abbott
 #' @export
 #' @examples

R/dist.R

@@ -3,7 +3,7 @@
 #' @description `r lifecycle::badge("questioning")`
 #' This function acts as a skeleton for a truncated distribution defined by
 #' model type, maximum value and model parameters. It is designed to be used
-#' with the output from `get_dist`.
+#' with the output from [get_dist()].
 #'
 #' @param n Numeric vector, number of samples to take (or days for the
 #' probability density).
@@ -177,7 +177,7 @@ dist_skel <- function(n, dist = FALSE, cum = TRUE, model,
 #'
 #' @description `r lifecycle::badge("stable")`
 #' Fits an integer adjusted exponential, gamma or lognormal distribution using
-#' `stan`.
+#' `{stan}`.
 #' @param values Numeric vector of values
 #'
 #' @param samples Numeric, number of samples to take. Must be >= 1000.
@@ -196,7 +196,7 @@ dist_skel <- function(n, dist = FALSE, cum = TRUE, model,
 #' @param verbose Logical, defaults to FALSE. Should verbose progress messages
 #' be printed.
 #'
-#' @return A `stan` fit of an interval censored distribution
+#' @return A `{stan}` fit of an interval censored distribution
 #' @author Sam Abbott
 #' @export
 #' @examples
@@ -286,7 +286,7 @@ dist_fit <- function(values = NULL, samples = 1000, cores = 1,
 #'
 #' @description `r lifecycle::badge("soft-deprecated")`
 #' Generates a distribution definition when only parameter estimates
-#' are available for gamma distributed parameters. See `rgamma` for
+#' are available for gamma distributed parameters. See [rgamma()] for
 #' distribution information.
 #'
 #' @param shape Numeric, shape parameter of the gamma distribution.
@@ -300,7 +300,7 @@ dist_fit <- function(values = NULL, samples = 1000, cores = 1,
 #' @param samples Numeric, number of sample distributions to generate.
 #'
 #' @importFrom truncnorm rtruncnorm
-#' @return A data.table defining the distribution as used by `dist_skel`
+#' @return A `<data.table>` defining the distribution as used by [dist_skel()]
 #' @export
 #' @inheritParams dist_skel
 #' @inheritParams lognorm_dist_def
@@ -370,7 +370,7 @@ gamma_dist_def <- function(shape, shape_sd,
 #'
 #' @description `r lifecycle::badge("soft-deprecated")`
 #' Generates a distribution definition when only parameter estimates
-#' are available for log normal distributed parameters. See `rlnorm` for
+#' are available for log normal distributed parameters. See [rlnorm()] for
 #' distribution information.
 #'
 #' @param mean Numeric, log mean parameter of the gamma distribution.
@@ -385,7 +385,7 @@ gamma_dist_def <- function(shape, shape_sd,
 #'
 #' @param to_log Logical, should parameters be logged before use.
 #'
-#' @return A data.table defining the distribution as used by `dist_skel`
+#' @return A `<data.table>` defining the distribution as used by [dist_skel()]
 #' @author Sam Abbott
 #' @importFrom truncnorm rtruncnorm
 #' @export
@@ -488,7 +488,7 @@ lognorm_dist_def <- function(mean, mean_sd,
 #' @param max_value Numeric, defaults to  the maximum value in the observed
 #' data. Maximum delay to  allow (added to output but does impact fitting).
 #'
-#' @return A `dist_spec` object summarising the bootstrapped distribution
+#' @return A `<dist_spec>` object summarising the bootstrapped distribution
 #' @author Sam Abbott
 #' @importFrom purrr transpose
 #' @importFrom future.apply future_lapply
@@ -587,16 +587,16 @@ bootstrapped_dist_fit <- function(values, dist = "lognormal",
 #'
 #' @description `r lifecycle::badge("maturing")`
 #' Estimate a log normal delay distribution from a vector of integer delays.
-#' Currently this function is a simple wrapper for `bootstrapped_dist_fit`.
+#' Currently this function is a simple wrapper for [bootstrapped_dist_fit()].
 #'
 #' @param delays Integer vector of delays
 #'
 #' @param ... Arguments to pass to internal methods.
 #'
-#' @return A `dist_spec` summarising the bootstrapped distribution
+#' @return A `<dist_spec>` summarising the bootstrapped distribution
 #' @author Sam Abbott
 #' @export
-#' @seealso bootstrapped_dist_fit
+#' @seealso [bootstrapped_dist_fit()]
 #' @examples
 #' \donttest{
 #' delays <- rlnorm(500, log(5), 1)
@@ -614,9 +614,9 @@ estimate_delay <- function(delays, ...) {
 #'
 #' @description `r lifecycle::badge("soft-deprecated")`
 #' Convolves cases by a PMF function. This function will soon be removed or
-#' replaced with a more robust `stan` implementation.
+#' replaced with a more robust `{stan}` implementation.
 #'
-#' @param cases A dataframe of cases (in date order) with the following
+#' @param cases A `<data.frame>` of cases (in date order) with the following
 #' variables: `date` and `cases`.
 #'
 #' @param max_value Numeric, maximum value to allow. Defaults to 120 days
@@ -639,7 +639,7 @@ estimate_delay <- function(delays, ...) {
 #' @param truncate_future Logical, should cases be truncated if they occur
 #' after the first date reported in the data. Defaults to `TRUE`.
 #'
-#' @return A `data.table` of cases by date of onset
+#' @return A `<data.table>` of cases by date of onset
 #' @export
 #' @importFrom purrr map_dfc
 #' @importFrom data.table data.table setorder
@@ -1054,7 +1054,7 @@ dist_spec <- function(mean, sd = 0, mean_sd = 0, sd_sd = 0,
 
 #' Creates a delay distribution as the sum of two other delay distributions
 #'
-#' This is done via convolution with `stats::convolve()`. Nonparametric delays
+#' This is done via convolution with [stats::convolve()]. Nonparametric delays
 #' that can be combined are processed together, and their cumulative
 #' distribution function is truncated at a specified tolerance level, ensuring
 #' numeric stability.
@@ -1109,7 +1109,7 @@ dist_spec_plus <- function(e1, e2, tolerance = 0.001) {
 
 #' Creates a delay distribution as the sum of two other delay distributions
 #'
-#' This is done via convolution with `stats::convolve()`. Nonparametric delays
+#' This is done via convolution with [stats::convolve()]. Nonparametric delays
 #' that can be combined are processed together, and their cumulative
 #' distribution function is truncated at a specified tolerance level, ensuring
 #' numeric stability.
@@ -1146,7 +1146,7 @@ dist_spec_plus <- function(e1, e2, tolerance = 0.001) {
 #' distributions to [epinow()] or [estimate_infections()].
 #'
 #' @param ... The delay distributions (from calls to [dist_spec()]) to combine
-#' @return Combined delay distributions (with class [dist_spec()]`)
+#' @return Combined delay distributions (with class `<dist_spec>`)
 #' @author Sebastian Funk
 #' @method c dist_spec
 #' @importFrom purrr transpose map
@@ -1174,7 +1174,7 @@ c.dist_spec <- function(...) {
 ##' This works out the mean of all the (parametric / nonparametric) delay
 ##' distributions combined in the passed [dist_spec()].
 ##'
-##' @param x The [dist_spec()] to use
+##' @param x The `<dist_spec>` to use
 ##' @param ... Not used
 ##' @return A vector of means.
 ##' @author Sebastian Funk
@@ -1226,7 +1226,7 @@ mean.dist_spec <- function(x, ...) {
 #'
 #' This displays the parameters of the uncertain and probability mass
 #' functions of fixed delay distributions combined in the passed [dist_spec()].
-#' @param x The [dist_spec()] to use
+#' @param x The `<dist_spec>` to use
 #' @param ... Not used
 #' @return invisible
 #' @author Sebastian Funk
@@ -1293,12 +1293,12 @@ print.dist_spec <- function(x, ...) {
 
 #' Plot PMF and CDF for a dist_spec object
 #'
-#' This function takes a [dist_spec] object and plots its probability mass
-#' function (PMF) and cumulative distribution function (CDF) using [ggplot2].
+#' This function takes a `<dist_spec>` object and plots its probability mass
+#' function (PMF) and cumulative distribution function (CDF) using `{ggplot2}`.
 #' Note that currently uncertainty in distributions is not plot.
 #'
-#' @param x A [dist_spec] object
-#' @param ... Additional arguments to pass to \code{ggplot}
+#' @param x A `<dist_spec>` object
+#' @param ... Additional arguments to pass to `{ggplot}`.
 #' @importFrom ggplot2 aes geom_col geom_step facet_wrap vars theme_bw
 #' @export
 #' @author Sam Abbott
@@ -1380,17 +1380,17 @@ plot.dist_spec <- function(x, ...) {
   return(plot)
 }
 
-##' Fix the parameters of a `dist_spec`
+##' Fix the parameters of a `<dist_spec>` object
 ##'
-##' If the given `dist_spec` has any uncertainty, it is removed and the
+##' If the given `<dist_spec>` has any uncertainty, it is removed and the
 ##' corresponding distribution converted into a fixed one.
-##' @return A `dist_spec` object without uncertainty
+##' @return A `<dist_spec>` object without uncertainty
 ##' @author Sebastian Funk
 ##' @export
-##' @param x A [dist_spec] object
+##' @param x A `<dist_spec>` object
 ##' @param strategy Character; either "mean" (use the mean estimates of the
 ##'   mean and standard deviation) or "sample" (randomly sample mean and
-##'   standard deviation from uncertainty given in the `dist_spec`)
+##'   standard deviation from uncertainty given in the `<dist_spec>`
 ##' @importFrom truncnorm rtruncnorm
 fix_dist <- function(x, strategy = c("mean", "sample")) {
   ## if x is fixed already we don't have to do anything