A collection of functions to simulate, estimate and forecast a wide range of regression based dynamic models for positive time series. 
	This package implements the results presented in Prass, T.S.; Carlos, J.H.; Taufemback, C.G. and Pumi, G. (2022). "Positive Time Series Regression" <arXiv:2201.03667>.

Includes a collection of functions presented in "Measuring stability in ecological systems without static equilibria" by Clark et al. (2022) <doi:10.1002/ecs2.4328> in Ecosphere.
	These can be used to estimate the parameters of a stochastic state space model (i.e. a model where
	a time series is observed with error). The goal of this package is to estimate the variability
	around a deterministic process, both in terms of observation error - i.e. variability due to
	imperfect observations that does not influence system state - and in terms of process noise - i.e.
	stochastic variation in the actual state of the process. Unlike classical methods for estimating
	variability, this package does not necessarily assume that the deterministic state is fixed (i.e.
	a fixed-point equilibrium), meaning that variability around a dynamic trajectory can be estimated
	(e.g. stochastic fluctuations during predator-prey dynamics).

Access the 'Public Transport Victoria' Timetable API 
    <https://www.ptv.vic.gov.au/footer/data-and-reporting/datasets/ptv-timetable-api/>,
    with results returned as familiar R data structures. Retrieve information on
    stops, routes, disruptions, departures, and more.

Parametric Time Warping aligns patterns, i.e. it aims to
        put corresponding features at the same locations. The algorithm
        searches for an optimal polynomial describing the warping. It
        is possible to align one sample to a reference, several samples
        to the same reference, or several samples to several
        references. One can choose between calculating individual
        warpings, or one global warping for a set of samples and one
        reference. Two optimization criteria are implemented: RMS (Root
        Mean Square error) and WCC (Weighted Cross Correlation). Both
	warping of peak profiles and of peak lists are supported. A
	vignette for the latter is contained in the inst/doc directory
	of the source package - the vignette source can be found on
	the package github site.