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".

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.; Pumi, G.; Taufemback, C.G. and Carlos, J.H. (2025). "Positive time series regression models: theoretical and computational aspects". Computational Statistics 40, 1185–1215. <doi:10.1007/s00180-024-01531-z>.

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).