# aedseo 1.1.0

## Features
* `combined_seasonal_output()` now includes a `seasonal_offset` logical variables in the output that estimates when the season has ended after the first
`seasonal_onset`. It can be controlled by the inputs: `burden_level_decrease` and `steps_with_decrease` (#93).

# aedseo 1.0.1

## Fixes
* `fit_growth_rate()` would incorrectly determine confidence intervals when using ATLAS BLAS/LAPACK (#89).

# aedseo 1.0.0

## Deprecations

## Features

* Added new arguments `incidence` and `incidence_denominator` to `to_time_series()` that allows the user to get output as incidence (#84).

* 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).

* Added new feature to estimate multiple waves in `combined_seasonal_output` (#77).

* Added `estimate_disease_threshold()` for users to easier estimate the disease specific threshold (#85).

## Improvements

* Observations are now divided into `cases` and `incidence`, which is implemented into all functions in the package. Cases are used as default, but if the user additionally inputs `population` the output will be incidence (#84).

## Minor changes

# aedseo 0.3.0

## 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).

# aedseo 0.1.2

## Minor changes

* Transferring maintainership of the R package to Lasse Engbo Christiansen.

# aedseo 0.1.1

## 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.

# aedseo 0.1.0

## 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.
