Standard environments for benchmarking the performance of RL algorithms.

A Julia package for simulation, verification and parameter estimation of Biochemical Networks with Approximate Bayesian Computation (ABC) methods.

Explores representations based on bag words

-

BHA analysis using PtFEM (currently used as a limited check on BHAtp's FEM results).

Graphical interface for BiochemicalAlgorithms.jl

-

-

-

-

Example data loading for ArviZ.jl

-

Hebrew orthography in Julia

-

Tables backed by AxisArrays

NIST XCOM x-ray attenuations in Julia.

AudioDB API client for Julia.

Tables with AxisIndices backend

Bayesian regression models for size-and-shape data

-

Non parametric productivity analysis under the B-disposal assumption

Standard data cleaning tools prior to generating BEAST xml file

Julia wrapper for ABIDES-Markets

Mirror of https://gitlab.kuleuven.be/numa/software/ApproximatingMapsBetweenLinearSpaces

Implementation of Asymptotic Numerical Method

Common solvers within orbital mechanics and astrodynamics.

A Julia implementation of the algorithm described in: Avrutin V et al. Calculation of homoclinic and heteroclinic orbits in 1D maps. Commun Nonlinear Sci Numer Simulat (2014), http://dx.doi.org/10.1016/j.cnsns.2014.07.008

FEM approach to estimate Bottom Hole Assembly building and dropping rates (aka "theoretical performance" under the "relaxed state" assumption).

Little extension for the https://github.com/svilupp/PromptingTools.jl to have streaming

Conduct one-way and multi-way anova in Julia with MixedModels.jl

Atomic Mass Evaluation dataset from AMDC: https://www-nds.iaea.org/amdc/

A julia package for working with text in the archaic Attic alphabet.

Live graphics with Luxor and Gtk.

Access data from the Allen Institute's open Neuropixels datasets

-

Load, format, analyze, and plot the Allen Neuropixels datasets

Some functions useful for small computations

-

Approximation Package for High-Dimensional Functions in Julia

This is a dimensionality reduction algorithm which has the goal of maintaining interpretability i.e we eliminate variables directly from potential models that don't seem to add any predictive power.