We provide methods to robustly estimate the parameters of stationary ergodic short-memory time series models in the potential presence of additive low-frequency contamination. The types of contaminati
Negative log likelihood functions for evaluating the biological models with data and incorporating stochasticity in the distribution generally, parameters to be estimated are packed into the object p
Comma separated values formatted file containing data used to estimate alpha1, alpha2, K1 and K2 (used to produce Figure 3 and SI Figures 1, 2 and 3). Columns as Figure1Data.csv, except time = time of
