Change log

v0.4.0

BUILD: Dropped legacy setuptools and migrated package build to hatch
BUILD: Removed setup.py, requirements.txt and MANIFEST in favour of pyproject.toml

Distributions classes now have python type hints.
Added distributions and time dependent arrivals via thinning example notebooks.
Added datasets module and function to load example NSPP dataset.
Distributions added
- Erlang (mean and stdev parameters)
- ErlangK (k and theta parameters)
- Poisson
- Beta
- Gamma
- Weibull
- PearsonV
- PearsonVI
- Discrete (values and observed frequency parameters)
- ContinuousEmpirical (linear interpolation between groups)
- RawEmpirical (resample with replacement from individual X's)
- TruncatedDistribution (arbitrary truncation of any distribution)
Added sim_tools.time_dependent module that contains NSPPThinning class for modelling time dependent arrival processes.
Updated test suite for distributions and thinning
Basic Jupyterbook of documentation.

Added sim_tools.distribution module. This contains classes representing popular sampling distributions for Discrete-event simulation. All classes encapsulate a numpy.random.Generator object, a random seed, and the parameters of a sampling distribution.
Python has been updated, tested, and patched for 3.10 and 3.11 as well as numpy 1.20+
Minor linting and code formatting improvement.