Provides adaptive trend estimation, cycle detection, Fourier harmonic
    selection, bootstrap confidence intervals, change-point detection, and
    rolling-origin forecasting. Supports LOESS (Locally Estimated Scatterplot
    Smoothing), GAM (Generalized Additive Model), and GAMM (Generalized Additive
    Mixed Model), and automatically handles irregular sampling using the
    Lomb-Scargle periodogram. Methods implemented in this package are described
    in Cleveland et al. (1990) <doi:10.2307/2289548>, Wood (2017)
    <doi:10.1201/9781315370279>, and Scargle (1982) <doi:10.1086/160554>.

Cyclomatic complexity is a software metric (measurement),
    used to indicate the complexity of a program. It is a quantitative
    measure of the number of linearly independent paths through a program's
    source code. It was developed by Thomas J. McCabe, Sr. in 1976.

Tools for calculating coordinate representations of
    hypocycloids, epicyloids, hypotrochoids, and epitrochoids
    (altogether called 'cycloids' here) with different scaling
    and positioning options. The cycloids can be visualised with
    any appropriate graphics function in R.

Modeling periodic mortality (or other time-to event) processes from right-censored data. Given observations of a process with a known period (e.g. 365 days, 24 hours), functions determine the number, intensity, timing, and duration of peaks of periods of elevated hazard within a period.  The underlying model is a mixed wrapped Cauchy function fitted using maximum likelihoods (details in Gurarie et al. (2020) <doi:10.1111/2041-210X.13305>). The development of these tools was motivated by the strongly seasonal mortality patterns observed in many wild animal populations. Thus, the respective periods of higher mortality can be identified as "mortality seasons".

This model fitting tool incorporates cyclic coordinate descent and
    majorization-minimization approaches to fit a variety of regression models
    found in large-scale observational healthcare data.  Implementations focus
    on computational optimization and fine-scale parallelization to yield
    efficient inference in massive datasets.  Please see:
    Suchard, Simpson, Zorych, Ryan and Madigan (2013) <doi:10.1145/2414416.2414791>.