  Birth-and-death processes (BDPs) form a class of continuous-time Markov
chains that are particularly suited to describing the changes in the size of a
population over time. Population-size-dependent BDPs (PSDBDPs) allow the rate
at which a population grows to depend on the current population size. The main
purpose of our new Python package BirDePy is to provide easy-to-use functions
that allow the parameters of discretely-observed PSDBDPs to be estimated. The
package can also be used to estimate parameters of continuously-observed
PSDBDPs, simulate sample paths, approximate transition probabilities, and
generate forecasts. We describe in detail several methods which have been
incorporated into BirDePy to achieve each of these tasks. The usage and
effectiveness of the package is demonstrated through a variety of examples of
PSDBDPs, as well as case studies involving annual population count data of two
endangered bird species.
