Statistical inference

NumPy/SciPy

jeanspy.model.FittableModel([...])	Subclassing interface for stateful likelihoods and prior terms.
jeanspy.model.FlatPriorModel(config[, ...])	Finite uniform bounds in explicitly named sampling coordinates.
jeanspy.model.PhotometryPriorModel(loc, scale)	Gaussian prior for log10(re_pc).
jeanspy.model.SphericalDSphEstimationModel(*args)	Spherical Jeans inference with a Gaussian LOS-velocity likelihood.
jeanspy.model.plummer_nfw_constant_anisotropy_model(...)	Compose Plummer + NFW + constant anisotropy with explicit finite priors.
jeanspy.model.AxisymmetricDSphEstimationModel(...)	Unbinned Gaussian LOS inference with the emcee sampler interface.
jeanspy.model.AxisymmetricKinematicData(...)	Finite matching 1-D observations, in pc and km/s, copied on construction.

Explicit NumPy/SciPy sampling coordinates

jeanspy.parameters.SamplingParameter(...[, ...])

Map one sampled coordinate to a physical model parameter.

emcee sampling and HDF5 storage

jeanspy.sampler.Sampler(model, p0_generator)

wrapper class for emcee.EnsembleSampler

NumPyro likelihoods, sampling and storage

jeanspy.sampler_numpyro.AxisymmetricJeansLikelihoodModel(...)	NumPyro likelihood for signed sky coordinates and zero mean streaming.
jeanspy.sampler_numpyro.JeansLikelihoodModel(...)	Callable spherical NumPyro model for line-of-sight velocity inference.
jeanspy.sampler_numpyro.NumPyroSampler(mcmc, ...)	Composition-based helper around numpyro.infer.MCMC.
jeanspy.sampler_numpyro.ParameterSpec(...[, ...])	Describe a parameter site and its physical representation.
jeanspy.sampler_numpyro.SamplerRunResult(...)	Report one NumPyroSampler run and persistence request.
jeanspy.sampler_numpyro.StorageBackend	Accepted sample-store names; install the dependencies for the selected backend.