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BaytesSMC.jl is a library to perform SMC proposal steps on `ModelWrapper` structs, see ModelWrappers.jl. Kernels that are defined in BaytesMCMC.jl and BaytesFilters.jl can be used inside this library.

A library to perform particle MCMC proposal steps for parameter in a `ModelWrapper` struct, see [ModelWrappers.jl](https://github.com/paschermayr/ModelWrappers.jl).

A package for the calculation of steady state coverages for a given gas composition, T and p

This package is a part of RSim, however, may also be used as an independent package for the calculation of source terms from an xml mechanism input file

Optimization library for Baytes modules

A library to perform MCMC proposal steps on `ModelWrapper` structs, see ModelWrappers.jl.

A library to perform particle filtering for one parameter in a `ModelWrapper` struct.

Wrappers to differentiate `ModelWrapper` structs, see ModelWrappers.jl.

Julia package with methods for computing options pricing, profit and loss

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Weibull Distribution allowing a Pareto approximation.

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Bayesian regression models for size-and-shape data

Computing the highest density region of a univariate, unimodal distribution in Julia

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Tools for GPs with Kronecker product kernels.

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Algorithms for the transport of mass and energy in the environment

Summarize a DataFrame, as might be needed for a manuscript's *Table 1*

Python extensions relating to Caesar.jl

Test cases for BAT.jl and for Bayesian Julia software in general

A thin wrapper around the python PyMBAR

Base package that ships symbols and functionality that is relevant to all or multiple packages in the ecosystem

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Adds support for parallelization for Taija packages.

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A package to handle data generated by QMSim

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Tiny Julia package that contains calculations for discrete choice models (used in economics).

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Direct Gaussian simulation solver for the GeoStats.jl framework

Dimension reduction regression for Julia

Implementation of Persistent Homology Transform

A Julia package for inverting Taylor series

A plugin of Express.jl for handling the ab initio software Quantum ESPRESSO

Compute the Pearson correlation to be used in Gaussian copulas

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Metida descriptive statistics.