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Birth-and-death processes (BDPs) form a class of continuous-time Markovchains that are particularly suited to describing the changes in the size of apopulation over time. Population-size-dependent BDPs (PSDBDPs) allow the rateat which a population grows to depend on the current population size. The mainpurpose of our new Python package BirDePy is to provide easy-to-use functionsthat allow the parameters of discretely-observed PSDBDPs to be estimated. Thepackage can also be used to estimate parameters of continuously-observedPSDBDPs, simulate sample paths, approximate transition probabilities, andgenerate forecasts. We describe in detail several methods which have beenincorporated into BirDePy to achieve each of these tasks. The usage andeffectiveness of the package is demonstrated through a variety of examples ofPSDBDPs, as well as case studies involving annual population count data of twoendangered bird species.

Birth-and-death Processes in Python: The BirDePy Package