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CausalInference.jl

Causal inference, graphical models and structure learning in Julia

Julia package for causal inference and analysis, graphical models and structure learning. This package contains code for the PC algorithm and the extended FCI algorithm, the score based greedy equivalence search (GES) algorithm, the Bayesian Causal Zig-Zag sampler and a function suite for adjustment set search.

Downloads: 0 This Week

Last Update: 2025-02-24
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2

NonlinearSolve.jl

High-performance and differentiation-enabled nonlinear solvers

...The package includes its own high-performance nonlinear solvers which include the ability to swap out to fast direct and iterative linear solvers, along with the ability to use sparse automatic differentiation for Jacobian construction and Jacobian-vector products. NonlinearSolve.jl interfaces with other packages of the Julia ecosystem to make it easy to test alternative solver packages and pass small types to control algorithm swapping. It also interfaces with the ModelingToolkit.jl world of symbolic modeling to allow for automatically generating high-performance code.

Downloads: 1 This Week

Last Update: 2025-11-14
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3

DynamicHMC

Implementation of robust dynamic Hamiltonian Monte Carlo methods

Implementation of robust dynamic Hamiltonian Monte Carlo methods in Julia. In contrast to frameworks that utilize a directed acyclic graph to build a posterior for a Bayesian model from small components, this package requires that you code a log-density function of the posterior in Julia. Derivatives can be provided manually, or using automatic differentiation. Consequently, this package requires that the user is comfortable with the basics of the theory of Bayesian inference, to the extent...

Downloads: 0 This Week

Last Update: 2025-05-07
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4

LinearSolve.jl

High-Performance Unified Interface for Linear Solvers in Julia

LinearSolve.jl is a unified interface for the linear solving packages of Julia. It interfaces with other packages of the Julia ecosystem to make it easy to test alternative solver packages and pass small types to control algorithm swapping. It also interfaces with the ModelingToolkit.jl world of symbolic modeling to allow for automatically generating high-performance code. Performance is key: the current methods are made to be highly performant on scalar and statically sized small problems, with options for large-scale systems. If you run into any performance issues, please file an issue.

Downloads: 0 This Week

Last Update: 2025-11-15
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