card (development version)
cosinor() unable to run on certain models based on y
cosinor_features() allows for assessing global/special
attributes of multiple component cosinor analysis
ggcosinor() is now functional for single and multiple
- 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
cosinor_zero_amplitude() test added, works for
- Population-mean cosinor analysis is added.
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
circ_compare_groups() helps to summarize
circadian data by an covariate and time. This is visualized using
ggcircadian(). Also includes the
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
confint currently function.
- Recurrent events can now be analyzed using a powerful function
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
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.