swap from macro to markdown for lifecycle

kaitejohnson · Nov 20, 2020 · 22920d7 · 22920d7
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -108,4 +108,3 @@ LinkingTo:
     rstan (>= 2.21.1),
     StanHeaders (>= 2.21.0-5)
 Language: en-GB
-RdMacros: lifecycle
diff --git a/R/adjust.R b/R/adjust.R
@@ -1,6 +1,6 @@
 #' Adjust from Case Counts by Infection Date to Date of Report
 #'
-#' @description  \lifecycle{soft-deprecated}
+#' @description  `r lifecycle::badge("soft-deprecated")`
 #' 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 `cases` variable.

diff --git a/R/create.R b/R/create.R
@@ -1,5 +1,5 @@
 #' Create Clean Reported Cases
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' Cleans a data frame of reported cases by replacing missing dates with 0 cases and applies an optional
 #' threshold at which point 0 cases are replaced with a moving average of observed cases. See `zero_threshold`
 #' for details.
@@ -40,7 +40,7 @@ create_clean_reported_cases <- function(reported_cases, horizon, zero_threshold
 
 #' Create Delay Shifted Cases
 #'
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' This functions creates a data frame of reported cases that has been smoothed using 
 #' a 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
@@ -86,7 +86,7 @@ create_shifted_cases <- function(reported_cases, shift,
 
 #' Construct the Required Future Rt assumption
 #'
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' 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 ("project"), to use the latest estimate based on partial data ("latest"),
@@ -114,7 +114,7 @@ create_future_rt <- function(future = "latest", delay = 0) {
 
 #' Create Time-varying Reproduction Number Data
 #'
-#' @description \lifecycle{stable}
+#' @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. 
@@ -178,7 +178,7 @@ create_rt_data <- function(rt = rt_opts(), breakpoints = NULL,
 }
 #' Create Back Calculation Data
 #'
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' 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()`. 
@@ -208,7 +208,7 @@ create_backcalc_data <- function(backcalc = backcalc_opts) {
 }
 #' Create Gaussian Process Data
 #'
-#' @description \lifecycle{stable}
+#' @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 
@@ -273,7 +273,7 @@ create_gp_data <- function(gp = gp_opts(), data) {
 
 #' Create Observation Model Settings
 #'
-#' @description \lifecycle{stable} 
+#' @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()`.
@@ -306,12 +306,12 @@ create_obs_model <- function(obs = obs_opts()) {
 }
 #' Create Stan Data Required for estimate_infections
 #'
-#' @description \lifecycle{stable} 
+#' @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.
 #' @param shifted_cases A dataframe of delay shifted cases
-#' @param truncation \lifecycle{experimental} A list of options as generated by `trunc_opts()`
+#' @param truncation `r lifecycle::badge("experimental")` A list of options as generated by `trunc_opts()`
 #' defining the truncation of observed data. Defaults to `trunc_opts()`. See `estimate_truncation()`
 #' for an approach to estimating truncation from data.
 #' @inheritParams create_gp_data
@@ -383,7 +383,7 @@ create_stan_data <- function(reported_cases, generation_time,
 }
 
 #' Create Initial Conditions Generating Function
-#' @description \lifecycle{stable} 
+#' @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 plausible values.
@@ -447,7 +447,7 @@ create_initial_conditions <- function(data) {
 
 #' Create a List of Stan Arguments
 #'
-#' @description \lifecycle{stable} 
+#' @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 initialisation. Initialisation defaults to random but it is expected that `create_initial_conditions`
 #' will be used.

diff --git a/R/data.R b/R/data.R
@@ -1,14 +1,14 @@
 #' Literature Estimates of Generation Times
 #'
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' Generation time estimates. See here for details: 
 #' https://github.com/epiforecasts/EpiNow2/blob/master/data-raw/generation-time.R
 #' @format A `data.table` of summarising the distribution
 "generation_times"
 
 #' Literature Estimates of Incubation Periods
 #'
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' Incubation period estimates. See here for details:
 #'  https://github.com/epiforecasts/EpiNow2/blob/master/data-raw/incubation-period.R
 #' @format A `data.table` of summarising the distribution
@@ -17,7 +17,7 @@
 
 #' Example Confirmed Case Data Set
 #'
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' An example data frame of observed cases
 #' @format A data frame containing cases reported on each date.
 "example_confirmed"
diff --git a/R/dist.R b/R/dist.R
@@ -1,6 +1,6 @@
 #' Distribution Skeleton
 #'
-#' @description \lifecycle{questioning}
+#' @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`.
@@ -121,7 +121,7 @@ dist_skel <- function(n, dist = FALSE, cum = TRUE, model,
 
 #' Fit an Integer Adjusted Exponential, Gamma or Lognormal distributions
 #'
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' Fits an integer adjusted exponential, gamma or lognormal distribution using 
 #' `stan`. 
 #' @param values Numeric vector of values
@@ -212,7 +212,7 @@ dist_fit <- function(values = NULL, samples = NULL, cores = 1,
 
 #' Generate a Gamma Distribution Definition Based on Parameter Estimates
 #'
-#' @description \lifecycle{soft-deprecated}
+#' @description `r lifecycle::badge("soft-deprecated")`
 #' Generates a distribution definition when only parameter estimates 
 #' are available for gamma distributed parameters. See `rgamma` for distribution information.
 #' @param shape Numeric, shape parameter of the gamma distribution.
@@ -268,7 +268,7 @@ gamma_dist_def <- function(shape, shape_sd,
 
 #' Generate a Log Normal Distribution Definition Based on Parameter Estimates
 #'
-#' @description \lifecycle{soft-deprecated}
+#' @description `r lifecycle::badge("soft-deprecated")`
 #' Generates a distribution definition when only parameter estimates 
 #' are available for log normal distributed parameters. See `rlnorm` for distribution information.
 #' @param mean Numeric, log mean parameter of the gamma distribution.
@@ -334,7 +334,7 @@ lognorm_dist_def <- function(mean, mean_sd,
 
 #' Fit a Subsampled Bootstrap to Integer Values and Summarise Distribution Parameters
 #'
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' Fits an integer adjusted distribution to a subsampled bootstrap of data and then integrates 
 #' the posterior samples into a single set of summary statistics. Can be used to generate a robust
 #' reporting delay that accounts for the fact the underlying delay likely varies over time or that 
@@ -432,7 +432,7 @@ bootstrapped_dist_fit <- function(values,  dist = "lognormal",
 
 #' Estimate a Delay Distribution
 #'
-#' @description \lifecycle{maturing}
+#' @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`. 
 #' @param delays Integer vector of delays
@@ -452,7 +452,7 @@ estimate_delay <- function(delays, ...) {
 
 #' Approximate Sampling a Distribution using Counts
 #'
-#' @description \lifecycle{soft-deprecated}
+#' @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.
 #' @param cases A dataframe of cases (in date order) with the following variables:
@@ -595,7 +595,7 @@ sample_approx_dist <- function(cases = NULL,
 
 #' Tune an Inverse Gamma to Achieve the Target Truncation
 #'
-#' @description \lifecycle{questioning}
+#' @description `r lifecycle::badge("questioning")`
 #' Allows an inverse gamma distribution to be. tuned so that less than 0.01 of its 
 #' probability mass function falls outside of the specified 
 #' bounds. This is required when using an inverse gamma prior, for example for a 

diff --git a/R/epinow-internal.R b/R/epinow-internal.R
@@ -1,6 +1,6 @@
 #' Updates Forecast Horizon Based on Input Data and Target
 #'
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' Makes sure that a forecast is returned for the user specified
 #' time period beyond the target date.
 #' @inheritParams setup_target_folder
@@ -16,7 +16,7 @@ update_horizon <- function(horizon, target_date, reported_cases) {
 
 #' Save Observed Data
 #'
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' Saves observed data to a target location if given.
 #' @inheritParams setup_target_folder
 #' @inheritParams epinow
@@ -36,7 +36,7 @@ save_input <- function(reported_cases, target_folder) {
 
 #' Save Estimated Infections
 #'
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' Saves output from `estimate_infections` to a target directory.
 #' @param estimates List of data frames as output by `estimate_infections`
 #' @param samples Logical, defaults to TRUE. Should samples be saved
@@ -65,7 +65,7 @@ save_estimate_infections <- function(estimates, target_folder = NULL,
 
 #' Save Forecast Infections
 #'
-#' @description \lifecycle{experimental}
+#' @description `r lifecycle::badge("experimental")`
 #' Saves the output from `forecast_infections` to a target directory.
 #' @param forecast A list of data frames as output by `forecast_infections`
 #' @inheritParams save_estimate_infections
@@ -85,7 +85,7 @@ save_forecast_infections <- function(forecast, target_folder = NULL, samples = T
 
 #' Estimate Cases by Report Date
 #'
-#' @description \lifecycle{questioning}
+#' @description `r lifecycle::badge("questioning")`
 #' Either extracts or converts reported cases from an input data table. For output from
 #' `estimate_infections` this is a simple filtering step but for output from `forecast_infection`
 #' this is currently an approximate convolution. This step is likely to be updated/deprecated 
@@ -158,7 +158,7 @@ estimates_by_report_date <- function(estimates, forecast, delays, CrIs = c(0.2,
 
 #' Copy Results From Dated Folder to Latest
 #'
-#' @description \lifecycle{questioning}
+#' @description `r lifecycle::badge("questioning")`
 #' Copies output from the dated folder to a latest folder. May be undergo changes in later releases.
 #' @param latest_folder Character string containing the path to the latest target folder. 
 #' As produced by `setup_target_folder`.
@@ -188,7 +188,7 @@ copy_results_to_latest <- function(target_folder = NULL, latest_folder = NULL) {
 
 #' Construct Output
 #'
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' Combines the output produced internally by `epinow` into a single list.
 #' @param estimated_reported_cases A list of dataframes as produced by 
 #' `estimates_by_report_date`.

diff --git a/R/epinow.R b/R/epinow.R
@@ -1,6 +1,6 @@
 #' Real-time Rt Estimation, Forecasting and Reporting
 #'
-#' @description \lifecycle{maturing}
+#' @description `r lifecycle::badge("maturing")`
 #' This function wraps the functionality of `estimate_infections()` and `forecast_infections()` in order
 #' to estimate Rt and cases by date of infection, forecast into these infections into the future. It also contains 
 #' additional functionality to convert forecasts to date of report and produce summary output useful for reporting 

diff --git a/R/estimate_infections.R b/R/estimate_infections.R
@@ -1,6 +1,6 @@
 #' Estimate Infections, the Time-Varying Reproduction Number and the Rate of Growth
 #'
-#' @description \lifecycle{maturing}
+#' @description `r lifecycle::badge("maturing")`
 #' Uses a non-parametric approach to reconstruct cases by date of infection from reported 
 #' cases. It uses either a generative Rt model or non-parametric back calculation to estimate underlying
 #' latent infections and then maps these infections to observed cases via uncertain reporting delays and a flexible
@@ -256,7 +256,7 @@ estimate_infections <- function(reported_cases,
 
 #' Fit a Stan Model using the NUTs sampler
 #'
-#' @description \lifecycle{maturing}
+#' @description `r lifecycle::badge("maturing")`
 #' Fits a stan model using `rstan::sampling`. Provides the optional ability to run chains using 
 #' `future` with error catching, timeouts and merging of completed chains.
 #' @param args List of stan arguments
@@ -365,7 +365,7 @@ fit_model_with_nuts <- function(args, future = FALSE, max_execution_time = Inf,
 
 #' Fit a Stan Model using Variational Inference
 #'
-#' @description \lifecycle{maturing}
+#' @description `r lifecycle::badge("maturing")`
 #' Fits a stan model using variational inference.
 #' @inheritParams fit_model_with_nuts
 #' @importFrom futile.logger flog.debug flog.info flog.error
@@ -425,7 +425,7 @@ fit_model_with_vb <- function(args, future = FALSE, id = "stan", verbose = FALSE
 
 #' Format Posterior Samples
 #'
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' Summaries posterior samples and adds additional custom variables. 
 #' @param posterior_samples A list of posterior samples as returned by `extract_parameter_samples`
 #' @param horizon Numeric, forecast horizon

diff --git a/R/estimate_truncation.R b/R/estimate_truncation.R
@@ -1,6 +1,6 @@
 #' Estimate Truncation of Observed Data
 #'
-#' @description \lifecycle{experimental}
+#' @description `r lifecycle::badge("experimental")`
 #' Estimates a truncation distribution from multiple snapshots of the same 
 #' data source over time. This distribution can then be used in `regional_epinow`,
 #' `epinow`, and `estimate_infections` to adjust for truncated data. See [here](https://gist.github.com/seabbs/176b0c7f83eab1a7192a25b28bbd116a) 
@@ -198,8 +198,8 @@ estimate_truncation <- function(obs, max_truncation = 10,
 
 #' Plot method for estimate_truncation
 #'
-#' @description \lifecycle{experimental}
-#' \code{plot} method for class "estimate_truncation". Returns 
+#' @description `r lifecycle::badge("experimental")`
+#' `plot` method for class "estimate_truncation". Returns 
 #' a plot faceted over each dataset used in fitting with the latest 
 #' observations as columns, the data observed at the time (and so truncated) 
 #' as dots and the truncation adjusted estimates as a ribbon.

diff --git a/R/extract.R b/R/extract.R
@@ -1,6 +1,6 @@
 #' Extract Samples for a Parameter from a Stan model
 #'
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' Extracts a single from a list of `stan` output and returns it as a `data.table`.
 #' @param param Character string indicating the parameter to extract
 #' @param samples Extracted stan model (using `rstan::extract`)
@@ -41,7 +41,7 @@ extract_static_parameter <- function(param, samples) {
 
 #' Extract Parameter Samples from a Stan Model
 #'
-#' @description \lifecycle{stable}
+#' @description `r lifecycle::badge("stable")`
 #' Extracts a custom set of parameters from a stan object and adds stratification and 
 #' dates where appropriate.
 #' @param stan_fit A fit Stan model as returned by `rstan:sampling`

diff --git a/R/forecast_infections.R b/R/forecast_infections.R
@@ -1,6 +1,6 @@
 #' Forecast Infections and the Time-Varying Reproduction Number
 #'
-#' @description \lifecycle{experimental}
+#' @description `r lifecycle::badge("experimental")`
 #' Provides optional tools for forecasting cases and Rt estimates using the timeseries methods
 #' (via the `EpiSoon` package). It requires the `Episoon` package. Installation instructions for the EpiSoon package are
 #' available [here](https://epiforecasts.io/EpiSoon/).