density_fun0

Default density function, following the syntax for priors in the BayesianTools package. Uses
flat priors for all paramters, within the given interval. Density function integrates to one.

optimization

time-series

MCMC

stability

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).

Adam Clark

pttstability

Particle-Takens Stability

density_fun0 function

<dl><dt>param</dt>
<dd>a vector model parameters</dd>
<dt>minv</dt>
<dd>a vector of minimum values for the interval</dd>
<dt>maxv</dt>
<dd>a vector of maximum values for the interval</dd></dl>

Arguments

Default density function for prior — density_fun0

<dl>

<dt>param</dt>
<dd>a vector model parameters</dd>


<dt>minv</dt>
<dd>a vector of minimum values for the interval</dd>


<dt>maxv</dt>
<dd>a vector of maximum values for the interval</dd>

</dl>

density_fun0: Default density function for prior

Description

Usage

Value

Arguments