-
cosinor()
to be expanded upon to include prediction, and integration into the tidymodels approach in theparsnip
package- Evaluation of plotting functions for cosinor models
- Confidence interval methods to be improved upon
-
Population cosinor analysis to be reworked for correct predictions and confidence interval estimates
-
The circadian-focused features are being deprecated in this upcoming release. The goal is to position functions in the appropriate package, with the key
cosinor()
functions to move to a separate package in a future release. -
The longitudinal event functions are being moved to a separate package to make maintenance more straightforward.
cosinor()
unable to run on certain models based on y values
cosinor_features()
allows for assessing global/special attributes of multiple component cosinor analysisggcosinor()
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 cosinorggcosinor()
accepts single modelsprint.cosinor()
andplot.cosinor()
functions addedcosinor_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 usingggcircadian()
. Also includes theggforest()
to create forest plots of odds ratios. This is dependent on thecirc_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 theggcosinorfit()
allows for assessing rhythmicity and confidence intervals of amplitude and acrophase of cosinor model. Basic methods for assessing the model, such asprint
,summary
,coef
, andconfint
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. Therecur_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 thecirc_center()
function to center a time series around an event, such as sunrise. A vignette has been added to review this data.