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).
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).
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).
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).
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.
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.