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>.

The cyclotomic numbers are complex numbers that can be
    thought of as the rational numbers extended with the roots of unity. They
    are represented exactly, enabling exact computations. They contain the
    Gaussian rationals (complex numbers with rational real and imaginary
    parts) as well as the square roots of all rational numbers. They also
    contain the sine and cosine of all rational multiples of pi. The
    algorithms implemented in this package are taken from the 'Haskell'
    package 'cyclotomic', whose algorithms are adapted from code by Martin
    Schoenert and Thomas Breuer in the 'GAP' project
    (<https://www.gap-system.org/>). Cyclotomic numbers have applications in
    number theory, algebraic geometry, algebraic number theory, coding 
    theory, and in the theory of graphs and combinatorics. They have 
    connections to the theory of modular functions and modular curves.