Changes in version 0.3.0 (2025-04-09) Deprecations Features - Added new argument population to to_time_series() and fit_growth_rate() that allows the user to add the background population connected to each observation (#83). - Added new argument use_offset to seasonal_onset() that allows the user to add the background population to adjust the growth rate estimations (#83). Improvements Minor changes Deprecations - aedseo() is now deprecated. Please use seasonal_onset() instead. A warning is shown when using aedseo() (#41). - tsd() is now deprecated. Please use to_time_series() instead. A warning is shown when using tsd() (#41). Features - Added the seasonal_burden_levels() function, which calculates burden levels based on data from previous seasons with two different methods; "peak_levels" or "intensity_levels" (#37). - Added the fit_percentiles() function, which optimises a user selected distribution and calculates the percentiles based on observations and weights. It is meant to be used within the seasonal_burden_levels() function (#35, #37) - Renamed fit_quantiles() to fit_percentiles() (#60). - Added combined_seasonal_output() as the main function to run both seasonal_onset() and seasonal_burden_levels() to get a combined result for the newest season (#44). - Added consecutive_growth_warnings() function to help the user with a method to define the disease-specific threshold (#80). - Added a new argument only_current_season to seasonal_onset(), seasonal_burden_levels() and combined_seasonal_output() which gives the possibility to either get output from only the current season or for all available seasons (#45). - Added historical_summary() which uses a tsd_onset object to summarise historical estimations (#75). - summary() can now summarise tsd_burden_level objects (#60). - plot() and autoplot() can now plot tsd_combined_seasonal_output and tsd_consecutive_growth_warning objects (#57, #80). - Added generate_seasonal_data() to generate synthetic data for testing and documentation purposes (#56). - Added seasonal_onset() as a replacement for the deprecated aedseo() function (#41). - Added to_time_series() as a replacement for the deprecated tsd() function (#41). Improvements - Enhanced clarity and user guidance in the vignettes: - vignette("generate_seasonal_wave"), - vignette("aedseo"), - vignette("seasonal_onset") - vignette("burden_levels") providing a comprehensive walkthrough of the application of the functions provided by the aedseo package with detailed explanations and illustrative examples (#56, #57, #58, #59, #60, #61). - Improved the autoplot() function which can now visualise dates as days, weeks and months on the x-axis with the time_interval argument (#56). - Improved the epi_calendar() function to work for a season spanning new year (#34). - Using predict() on tsd_onset objects now uses the same time-scale as the given object (#61). That is, the time_interval attribute controls if predictions are by "days", "weeks" or "months". - The aedseo() function now allows for the choice of adding season as an input argument (#34). - {checkmate} assertions have been added to enhance user feedback with clearer error messages and to ensure functions operate correctly by validating inputs (#33). - Improved the aedseo() function to work with NA values. The user now defines how many NA values the function should allow in each window (#32). Minor changes - Added Sofia Myrup Otero as an author of the R package (#55). - Added Rasmus Skytte Randløv as a reviewer of the R package (#55). - The disease_threshold argument now reflects the disease threshold in one time step. If the total number of cases in a window of size k exceeds disease_threshold * k, a seasonal onset alarm can be triggered (#32). Changes in version 0.1.2 (2023-11-27) Minor changes - Transferring maintainership of the R package to Lasse Engbo Christiansen. Changes in version 0.1.1 (2023-11-16) Improvements - Enhanced clarity and user guidance in the introductory vignette, providing a more comprehensive walkthrough of the application of the 'aeddo' algorithm on time series data with detailed explanations and illustrative examples. Minor changes - Updated LICENSE.md to have Statens Serum Institut as a copyright holder. - Fixed installation guide for the development version in the README.Rmd and README.md - Added Lasse Engbo Christiansen as an author of the R package. - Added a new function epi_calendar() that determines the epidemiological season based on a given date, allowing users to easily categorize dates within or outside specified seasons. - Introduced additional visualizations in the autoplot() method, enhancing the capabilities of the plot() method with new displays of observed cases and growth rates. Changes in version 0.1.0 (2023-11-07) Features - Added the aedseo function, which automates the early detection of seasonal epidemic onsets by estimating growth rates for consecutive time intervals and calculating the Sum of Cases (sum_of_cases). - Introduced autoplot and plot methods for visualizing aedseo and aedseo_tsd objects. These functions allow you to create insightful ggplot2 plots for your data. - Included the fit_growth_rate function, enabling users to fit growth rate models to time series observations. - Introduced the predict method for aedseo objects, which allows you to predict observations for future time steps given the growth rates. - Added the summary method for aedseo objects, providing a comprehensive summary of the results. - Introduced the tsd function, allowing users to create S3 aedseo_tsd (time-series data) objects from observed data and corresponding dates.