Bayesian inference for Discrete state-space Partially Observed Markov Processes in Julia. See the docs:

Bayesian optimal design of experiments in Julia. Note: This is just a mirror of the main branch for the Julia General registry. Development happens at sourcehut. Please report any issues over there.

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Julia bindings for the LibRaw library

A lightweight Julia package for eQTL genome scans near real-time.

Support for orthogonal polynomial-based spaces in ApproxFun

Estimation of calibration errors using Distributions.jl

Operator pack for Jets.jl. Part of the COFII framework.

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Create population genetics simulations

HYMOD model implementation in Julia

Implementation of basic hyperbolic geometry (Poincare disc model)

Minesweeper in Julia

Creating Latin squares and pairs of orthogonal Latin squares

Analyzing ranked/ordinal preference data.

Empirical distribution fitting for Rasters.jl.

Geostatistical estimation solvers for the GeoStats.jl framework

Test images for evaluating MPI simulation frameworks.

Fast `StatsBase.countmap` for small types on the GPU via CUDA.jl

Artifacts for Geospatial Data Science

Automatically mirror Julia package repositories

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Tools for the computation of canopy optical parameters

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A Julia package for the ACT-R cognitive architecture

Shared package to handle constraints for the different solvers from JuliaConstraints

Polyomino.jl - Polyomino generation and chess algorithms implemented in Julia

A Julia package for Generative Topographic Mapping

Support for spaces with singularities in ApproxFun

Methods to find zeros (roots) of polynomials over given domains

A Julia package to numerically approximate 1d function integrals using any of a variety of different methods.

The package provides the DataFrame with data on Isotopes.

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Package for computing invariant sets of general discrete-time dynamical systems with controls and uncertainties using graph-based algorithms.

Julia package with Daubechies wavelets on an interval

Linear combinations of parameters

Compute MLEs for linear covariance models