Fits the Poisson-Tweedie generalized linear mixed model
    described in Signorelli et al. (2021, <doi:10.1177/1471082X20936017>).
    Likelihood approximation based on adaptive Gauss Hermite quadrature
    rule.

Functions used for analyzing count data, mostly crime counts. Includes checking difference in two Poisson counts (e-test), checking the fit for a Poisson distribution, small sample tests for counts in bins, Weighted Displacement Difference test (Wheeler and Ratcliffe, 2018) <doi:10.1186/s40163-018-0085-5>, to evaluate crime changes over time in treated/control areas. Additionally includes functions for aggregating spatial data and spatial feature engineering.

Fits and analyses time dependent marked point process models with an emphasis on earthquake modelling. For a more detailed introduction to the package, see the topic "PtProcess". A list of recent changes can be found in the topic "Change Log".

Utility functions produced specifically for (but not limited
    to) working with 'ProjectTemplate' data pipelines. This package helps
    to quickly create and manage sequentially numbered scripts, quickly
    set up logging with 'log4r' and functions to help debug and monitor
    procedures.

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).