Julia library for fitting and analyzing state space models. Provides efficient implementations of various SSMs including the canonical Gaussian LDS (Kalman Filter/Smoother), Poisson LDS, HMMs, etc.

TensorNetworkCodes is a Julia library developed to support the following research: https://arxiv.org/abs/2109.11996

Numerical Methods for Vlasov-Poisson and Vlasov-Maxwell Systems

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Package for unfolding of complex 3-D geometries.

Experimental Approach to McCormick Relaxation Source-Code Transformation

Model comparison functions for statistical models as used in StatisticalRethinking. Version 2 will likely use or be based on ParetoSmooth.jl.

High performance hybrid Monte Carlo simulation

An unofficial implementation of publicly available approximated polynomial models for NASA's Generic Transport Model aircraft.

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Cumulants based features selection and outlier detection

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Utilities to read/write extended GSLIB files in Julia

Julia implementation of the Gumbel-Softmax reparametrization trick compatible with Zygote and ForwardDiff

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Http://proceedings.mlr.press/v119/bruinsma20a.html

Plot critical difference diagrams in Julia

An extension package of PowerModels.jl for Harmonic (Optimal) Power Network

Method to estimate phylogenetic networks from algebraic invariants

Mixed model solution for replicate designed bioequivalence study.

Conversions between Distributions.jl distributions and MeasureTheory.jl measures.

A Julia package for modelling Algorithmic Recourse Dynamics.

Modular/extensible Julia library for Computer Adaptive Tests

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Pipeline for debiasing and improving error bar estimates for photometry on top of structured/filamentary background.

Fast implementations of FastICA and DUET for blind source separation

Independent Hypothesis Weighting for multiple testing with side-information in Julia

Tools to specify and simulate individual based models, using applied category theory

Causal Inference using Gaussian Processes with Structured Latent Confounders. Estimate treatment effects with Gaussian processes.

Toolbox for categorical time-series analysis.

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Drawing functions for `SimpleGraphs`

Hypothesis tests of calibration.

Implementation of a drone surveillance problem with POMDPs.jl

A thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)

Sampling quantum phase space distributions

Reinforcement learning framework to find and analyze the likeliest failures of a system under test.

BioCCP.jl exploits the Coupon Collector Problem for sample size determination in combinatorial biotechnology.

A companion package for Microbiome.jl for working with the Biobakery family of computational tools

Unfortunately, FMUs (fmi-standard.org) are not differentiable by design. To enable their full potential inside Julia, FMISensitivity.jl makes FMUs fully differentiable, regarding to: states and derivatives | inputs, outputs and other observable variables | parameters | event indicators | explicit time | state change sensitivity by event