NEWS.md
In card: Cardiovascular and Autonomic Research Design

card (development version)

cosinor() unable to run on certain models based on y values
`ggcosinor

cosinor_features() allows for assessing global/special attributes of multiple component cosinor analysis
ggcosinor() is now functional for single and multiple component analysis
Sequential model building can be performed with build_sequential_models(), however it is in a list format and will likely be updated to be more "tidy" in the future
Confidence interval methods now work for population-mean cosinor, including summary function
ggpopcosinor() can show the cosinors for individuals across a population, along with mean and predicted cosinor
ggcosinor() accepts single models
print.cosinor() and plot.cosinor() functions added
cosinor_zero_amplitude() test added, works for individual cosinor.
Population-mean cosinor analysis is added. cosinor() now takes the argument of for individuals. The individual cosinor methods generally work, but may not yet be accurate.
Circadian rhythm analysis has also created an initial family of functions that will work to simplify the process of analyzing 24-hour data. The circ_compare_groups() helps to summarize circadian data by an covariate and time. This is visualized using ggcircadian(). Also includes the ggforest() to create forest plots of odds ratios. This is dependent on the circ_odds() function to generate odds ratios by time.
An important regression function, built with the hardhat package from tidymodels, cosinor() introduced as a new function to allow for diagnostic analysis of circadian patterns. Although the algorithm is well known, having an implementation in R allows potential diagnostics. This includes the ggcosinorfit() allows for assessing rhythmicity and confidence intervals of amplitude and acrophase of cosinor model. Basic methods for assessing the model, such as print, summary, coef, and confint currently function.
Recurrent events can now be analyzed using a powerful function called recur_survival_table(), which allows for redesigning longitudinal data tables into a model appropriate for analysis. It is built to extend survival analyses. The recur_summary_table() function allows for reviewing the findings from recurrent events by category to help understand event strata.
The circ_sun() function allows for identifying the sunrise and sunset times based on geographical location. This is intended to couple with the circ_center() function to center a time series around an event, such as sunrise. A vignette has been added to review this data.