card (development version)
Next Steps
Bugs
cosinor()
unable to run on certain models based on y
values
- `ggcosinor
Features
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.