`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.