Extend 'cxxfunction' by saving the dynamic shared objects
        for reusing across R sessions.

An interface to the cycle routing/data services provided by
    'CycleStreets', a not-for-profit social enterprise and advocacy
    organisation.  The application programming interfaces (APIs) provided
    by 'CycleStreets' are documented at
    (<https://www.cyclestreets.net/api/>).  The focus of this package is
    the journey planning API, which aims to emulate the routes taken by a
    knowledgeable cyclist.  An innovative feature of the routing service
    of its provision of fastest, quietest and balanced profiles.  These
    represent routes taken to minimise time, avoid traffic and compromise
    between the two, respectively.

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".