Remove badges from functions and rename

R/aedseo.R

@@ -1,14 +1,13 @@
 #' Automated and Early Detection of Seasonal Epidemic Onset
 #'
 #' @description
-#' `r lifecycle::badge("stable")`
 #'
 #' This function performs automated and early detection of seasonal epidemic
 #' onsets (aedseo) on a time series dataset. It estimates growth rates for
 #' consecutive time intervals and calculates the sum of cases (sum_of_cases).
 #'
 #' @param tsd A `aedseo_tsd` object containing time series data with 'time' and
-#' 'observed.'
+#' 'observation.'
 #' @param k An integer specifying the window size for modeling growth rates.
 #' @param level The confidence level for parameter estimates, a numeric value
 #' between 0 and 1.
@@ -28,7 +27,7 @@
 #'
 #' @return A `aedseo` object containing:
 #'   - 'reference_time': The time point for which the growth rate is estimated.
-#'   - 'observed': The observed value in the reference time point.
+#'   - 'observation': The observation in the reference time point.
 #'   - 'season': The stratification of observables in corresponding seasons.
 #'   - 'growth_rate': The estimated growth rate.
 #'   - 'lower_growth_rate': The lower bound of the growth rate's confidence
@@ -49,7 +48,7 @@
 #' @examples
 #' # Create a tibble object from sample data
 #' tsd_data <- tsd(
-#'   observed = c(100, 120, 150, 180, 220, 270),
+#'   observation = c(100, 120, 150, 180, 220, 270),
 #'   time = as.Date(c(
 #'     "2023-01-01",
 #'     "2023-01-02",
@@ -90,7 +89,7 @@ aedseo <- function(
   coll <- checkmate::makeAssertCollection()
   checkmate::assert_data_frame(tsd, add = coll)
   checkmate::assert_class(tsd, "aedseo_tsd", add = coll)
-  checkmate::assert_names(colnames(tsd), identical.to = c("time", "observed"), add = coll)
+  checkmate::assert_names(colnames(tsd), identical.to = c("time", "observation"), add = coll)
   checkmate::assert_numeric(level, lower = 0, upper = 1, add = coll)
   checkmate::assert_numeric(na_fraction_allowed, lower = 0, upper = 1,
                             add = coll)
@@ -130,7 +129,7 @@ aedseo <- function(
     } else {
       # Calculate growth rates
       growth_rates <- fit_growth_rate(
-        observations = obs_iter$observed,
+        observations = obs_iter$observation,
         level = level,
         family = family
       )
@@ -140,7 +139,7 @@ aedseo <- function(
     growth_warning <- growth_rates$estimate[2] > 0
 
     # Calculate Sum of Cases (sum_of_cases)
-    sum_of_cases <- base::sum(obs_iter$observed, na.rm = TRUE)
+    sum_of_cases <- base::sum(obs_iter$observation, na.rm = TRUE)
 
     # Evaluate if sum_of_cases exceeds disease_threshold
     sum_of_cases_warning <- sum_of_cases > (disease_threshold * k)
@@ -153,7 +152,7 @@ aedseo <- function(
       res,
       tibble::tibble(
         reference_time = tsd$time[i],
-        observed = tsd$observed[i],
+        observation = tsd$observation[i],
         season = tsd$season[i],
         growth_rate = growth_rates$estimate[1],
         lower_growth_rate = growth_rates$estimate[2],

R/autoplot.R

@@ -1,7 +1,6 @@
 #' Create a complete 'ggplot' appropriate to a particular data type
 #'
 #' @description
-#' `r lifecycle::badge("stable")`
 #'
 #'  This function generates a complete 'ggplot' object suitable for
 #'  visualizing time series data in an `aedseo_tsd` object. It creates a line
@@ -23,7 +22,7 @@
 #' @examples
 #' # Create an example aedseo_tsd object
 #' aedseo_tsd_object <- tsd(
-#'   observed = c(100, 120, 150, 180, 220, 270),
+#'   observation = c(100, 120, 150, 180, 220, 270),
 #'   time = as.Date(c(
 #'     "2023-01-01",
 #'     "2023-01-02",
@@ -62,7 +61,7 @@ autoplot.aedseo_tsd <- function(object, ...) {
     ggplot2::ggplot(
       mapping = ggplot2::aes(
         x = .data$time,
-        y = .data$observed
+        y = .data$observation
       )
     ) +
     ggplot2::geom_point() +
@@ -87,7 +86,7 @@ autoplot.aedseo <- function(
         ggplot2::ggplot(
           mapping = ggplot2::aes(
             x = .data$reference_time,
-            y = .data$observed
+            y = .data$observation
           )
         ) +
         ggplot2::geom_point(

R/epi_calendar.R

@@ -1,7 +1,6 @@
 #' Determine Epidemiological Season
 #'
 #' @description
-#' `r lifecycle::badge("stable")`
 #'
 #' This function identifies the epidemiological season, (must span new year)
 #' to which a given date belongs.

R/fit_growth_rate.R

@@ -1,7 +1,6 @@
 #' Fit a growth rate model to time series observations.
 #'
 #' @description
-#' `r lifecycle::badge("stable")`
 #'
 #' This function fits a growth rate model to time series observations and
 #' provides parameter estimates along with confidence intervals.

R/fit_quantiles.R

@@ -1,12 +1,11 @@
 #' Fits weighted observations to distribution and returns quantiles
 #'
 #' @description
-#' `r lifecycle::badge("stable")`
 #'
 #' This function calculates the quantiles of weighted time series observations. The output contains the quantiles
 #' from the fitted distribution.
 #'
-#' @param weighted_observations A tibble containing two columns of length n; `observed`, which contains the data
+#' @param weighted_observations A tibble containing two columns of length n; `observation`, which contains the data
 #' points, and `weight`, which is the importance assigned to the observation. Higher weights indicate that an
 #' observation has more influence on the model outcome, while lower weights reduce its impact.
 #' @param conf_levels A numeric vector specifying the confidence levels for parameter estimates. The values have
@@ -49,7 +48,7 @@
 #'
 #' # Add into a tibble with decreasing weight for older seasons
 #' data_input <- tibble::tibble(
-#'   observed = observations,
+#'   observation = observations,
 #'   weight = 0.8^rep(season_num_rev, each = obs)
 #' )
 #'
@@ -78,7 +77,7 @@ fit_quantiles <- function(
   checkmate::assert_numeric(conf_levels, lower = 0, upper = 1,
                             unique = TRUE, sorted = TRUE, add = coll)
   checkmate::assert_names(colnames(weighted_observations),
-                          identical.to = c("observed", "weight"), add = coll)
+                          identical.to = c("observation", "weight"), add = coll)
   checkmate::assert_numeric(lower_optim, add = coll)
   checkmate::assert_numeric(upper_optim, add = coll)
   checkmate::reportAssertions(coll)
@@ -99,16 +98,16 @@ fit_quantiles <- function(
   # The weighted negative loglikelihood function
   nll <- function(par, weighted_observations, family = family) {
     log_probability <- switch(family,
-      weibull = stats::dweibull(weighted_observations$observed, shape = exp(par[1]), scale = exp(par[2]),
+      weibull = stats::dweibull(weighted_observations$observation, shape = exp(par[1]), scale = exp(par[2]),
                                 log = TRUE),
-      lnorm = stats::dlnorm(weighted_observations$observed, meanlog =  par[1], sdlog = par[2], log = TRUE),
-      exp = stats::dexp(weighted_observations$observed, rate = exp(par[1]), log = TRUE)
+      lnorm = stats::dlnorm(weighted_observations$observation, meanlog =  par[1], sdlog = par[2], log = TRUE),
+      exp = stats::dexp(weighted_observations$observation, rate = exp(par[1]), log = TRUE)
     )
     return(-sum(log_probability * weighted_observations$weight))
   }
 
   # Run optimisation for weighted observations
-  optim_obj <- stats::optim(par = init_par_fun(family = family, observations = weighted_observations$observed),
+  optim_obj <- stats::optim(par = init_par_fun(family = family, observations = weighted_observations$observation),
                             fn = nll,
                             weighted_observations = weighted_observations,
                             family = family,

R/plot.R

@@ -1,7 +1,6 @@
 #' Create a complete 'ggplot' appropriate to a particular data type
 #'
 #' @description
-#' `r lifecycle::badge("stable")`
 #'
 #'  This function generates a complete 'ggplot' object suitable for
 #'  visualizing time series data in an `aedseo_tsd` object. It creates a line
@@ -19,7 +18,7 @@
 #' @examples
 #' # Create an example aedseo_tsd object
 #' aedseo_tsd_object <- tsd(
-#'   observed = c(100, 120, 150, 180, 220, 270),
+#'   observation = c(100, 120, 150, 180, 220, 270),
 #'   time = as.Date(c(
 #'     "2023-01-01",
 #'     "2023-01-02",

R/predict.R

@@ -1,7 +1,6 @@
 #' Predict Growth Rates for Future Time Steps
 #'
 #' @description
-#' `r lifecycle::badge("stable")`
 #'
 #' This function is used to predict future growth rates based on a model object
 #' created using the 'aedseo' package. It takes the model object and the number
@@ -25,7 +24,7 @@
 #' @examples
 #' # Analyze the data using the aedseo package
 #' tsd_data <- tsd(
-#'   observed = c(100, 120, 150, 180, 220, 270),
+#'   observation = c(100, 120, 150, 180, 220, 270),
 #'   time = as.Date(c(
 #'     "2023-01-01",
 #'     "2023-01-02",
@@ -56,9 +55,9 @@ predict.aedseo <- function(object, n_step = 3, ...) {
     dplyr::reframe(
       t = 0:n_step,
       time = .data$reference_time + t,
-      estimate = exp(log(.data$observed) + .data$growth_rate * t),
-      lower = exp(log(.data$observed) + .data$lower_growth_rate * t),
-      upper = exp(log(.data$observed) + .data$upper_growth_rate * t)
+      estimate = exp(log(.data$observation) + .data$growth_rate * t),
+      lower = exp(log(.data$observation) + .data$lower_growth_rate * t),
+      upper = exp(log(.data$observation) + .data$upper_growth_rate * t)
     )
 
   # Extract the attributes from the object

R/seasonal_burden_levels.R

@@ -1,12 +1,11 @@
 #' Compute burden levels from seasonal time series observations.
 #'
 #' @description
-#' `r lifecycle::badge("stable")`
 #'
 #' This function calculates the burden levels of time series of observations that are stratified by season.
 #' It uses the previous seasons to calculate the levels of the newest season.
 #'
-#' @param tsd A `aedseo_tsd` object containing time series data with 'time' and 'observed'.
+#' @param tsd A `aedseo_tsd` object containing time series data with 'time' and 'observation'.
 #' @param season_weeks A numeric vector of length 2, `c(start, end)`, with the start and end weeks of the seasons to
 #' stratify the observations by. Must span the new year; e.g.: `season_weeks = c(21, 20)`.
 #' NOTE: The data must include data for a complete previous season to make predictions for the newest season.
@@ -67,10 +66,10 @@
 #'   add_to_max <- c(50, 100, 200, 100)
 #'   peak <- add_to_max + max(random_increasing_obs)
 #'
-#'   # Combine into a single observed sequence
-#'   observed <- c(random_increasing_obs, peak, random_decreasing_obs)
+#'   # Combine into a single observations sequence
+#'   observations <- c(random_increasing_obs, peak, random_decreasing_obs)
 #'
-#'  return(observed)
+#'  return(observations)
 #' }
 #'
 #' season_1 <- generate_flu_season()
@@ -84,7 +83,7 @@
 #'                          by = "week")
 #'
 #' tsd_data <- tsd(
-#'   observed = c(season_1, season_2),
+#'   observation = c(season_1, season_2),
 #'   time = as.Date(weekly_dates),
 #'   time_interval = "week"
 #' )
@@ -106,7 +105,7 @@ seasonal_burden_levels <- function(
   coll <- checkmate::makeAssertCollection()
   checkmate::assert_data_frame(tsd, add = coll)
   checkmate::assert_class(tsd, "aedseo_tsd", add = coll)
-  checkmate::assert_names(colnames(tsd), identical.to = c("time", "observed"), add = coll)
+  checkmate::assert_names(colnames(tsd), identical.to = c("time", "observation"), add = coll)
   checkmate::assert_integerish(season_weeks, len = 2, lower = 1, upper = 53,
                                null.ok = FALSE, add = coll)
   checkmate::assert_numeric(decay_factor, lower = 0, upper = 1, len = 1, add = coll)
@@ -139,12 +138,12 @@ seasonal_burden_levels <- function(
   # Select n_peak highest observations and filter observations >= disease_threshold
   season_observations_and_weights <- weighted_seasonal_tsd |>
     dplyr::select(-c("year", "time")) |>
-    dplyr::filter(.data$observed >= disease_threshold) |>
-    dplyr::slice_max(.data$observed, n = n_peak, with_ties = FALSE, by = "season")
+    dplyr::filter(.data$observation >= disease_threshold) |>
+    dplyr::slice_max(.data$observation, n = n_peak, with_ties = FALSE, by = "season")
 
   # Run quantiles_fit function
   quantiles_fit <- season_observations_and_weights |>
-    dplyr::select("observed", "weight") |>
+    dplyr::select("observation", "weight") |>
     fit_quantiles(weighted_observations = _, conf_levels = conf_levels, ...)
 
   # If method intensity_levels was chosen; use the high level from the `fit_quantiles` function as the high

R/summary.R

@@ -1,7 +1,6 @@
 #' Summary method for aedseo objects
 #'
 #' @description
-#' `r lifecycle::badge("stable")`
 #'
 #' Summarize the results of an aedseo analysis, including the latest growth rate
 #' estimate, the confidence interval, and information about growth warnings.
@@ -18,7 +17,7 @@
 #' @examples
 #' # Create a tsibble object from sample data
 #' tsd_data <- tsd(
-#'   observed = c(100, 120, 150, 180, 220, 270),
+#'   observation = c(100, 120, 150, 180, 220, 270),
 #'   time = as.Date(c(
 #'     "2023-01-01",
 #'     "2023-01-02",

R/tsd.R

@@ -1,28 +1,26 @@
-#' Create a S3 `aedseo_tsd` (time-series data) object from observed data
-#' and corresponding dates.
+#' Create a S3 `aedseo_tsd` (time-series data) object from observed data and corresponding dates.
 #'
 #' @description
-#' `r lifecycle::badge("stable")`
 #'
-#' This function takes observed data and the corresponding date vector and
+#' This function takes observations and the corresponding date vector and
 #' converts it into a `aedseo_tsd` object, which is a time series
 #' data structure that can be used for time series analysis.
 #'
-#' @param observed A numeric vector containing the observations.
+#' @param observation A numeric vector containing the observations.
 #' @param time A date vector containing the corresponding dates.
 #' @param time_interval A character vector specifying the time interval.
 #' Choose between "day," "week," or "month."
 #'
 #' @return A `aedseo_tsd` object containing:
-#'   - 'time': The time point for for where the observations is observed.
-#'   - 'observed': The observed value at the time point.
+#'   - 'time': The time point for for when the observation is observed.
+#'   - 'observation': The observed value at the time point.
 #'
 #' @export
 #'
 #' @examples
 #' # Create a `aedseo_tsd` object from daily data
 #' daily_tsd <- tsd(
-#'   observed = c(10, 15, 20, 18),
+#'   observation = c(10, 15, 20, 18),
 #'   time = as.Date(
 #'     c("2023-01-01", "2023-01-02", "2023-01-03", "2023-01-04")
 #'   ),
@@ -31,7 +29,7 @@
 #'
 #' # Create a `aedseo_tsd` object from weekly data
 #' weekly_tsd <- tsd(
-#'   observed = c(100, 120, 130),
+#'   observation = c(100, 120, 130),
 #'   time = as.Date(
 #'     c("2023-01-01", "2023-01-08", "2023-01-15")
 #'   ),
@@ -40,18 +38,18 @@
 #'
 #' # Create a `aedseo_tsd` object from monthly data
 #' monthly_tsd <- tsd(
-#'   observed = c(500, 520, 540),
+#'   observation = c(500, 520, 540),
 #'   time = as.Date(
 #'     c("2023-01-01", "2023-02-01", "2023-03-01")
 #'   ),
 #'   time_interval = "month"
 #' )
 #'
-tsd <- function(observed, time, time_interval = c("day", "week", "month")) {
+tsd <- function(observation, time, time_interval = c("day", "week", "month")) {
   # Check input arguments
   coll <- checkmate::makeAssertCollection()
   checkmate::assert_date(time, add = coll)
-  checkmate::assert_numeric(observed, add = coll)
+  checkmate::assert_numeric(observation, add = coll)
   checkmate::reportAssertions(coll)
 
   # Throw an error if any of the inputs are not supported
@@ -60,7 +58,7 @@ tsd <- function(observed, time, time_interval = c("day", "week", "month")) {
   # Collect the input in a tibble
   tbl <- tibble::tibble(
     time = time,
-    observed = observed
+    observation = observation
   )
 
   # Create the time series data object