Data-driven models for extremely precise radial velocity (EPRV) spectra

Arrays with arbitrarily nested named components.

Easily and efficiently memoize any function, closure, or callable object in Julia.

Save outputs from (expensive) computations.

Julia package for automated Bayesian inference on a factor graph with reactive message passing

ICDS User Guide

Calculate support points for a sample of data

Octofitter is a Julia package for performing Bayesian inference against a wide variety of exoplanet and binary star data.

Template for a course website based on https://computationalthinking.mit.edu

Optimally weighted PCA for samples with heterogeneous quality

Functions useful when using Pluto in teaching.

Fit, evaluate, and visualise generalised additive models (GAMs) in native Julia

Slides, paper notes, class notes, blog posts, and research on ML 📉, statistics 📊, and AI 🤖.

A Julia framework for invertible neural networks

Simple chains

Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia

Analyzing Radial Velocity Elements

Julia enhancement proposal (Julep) for implicit per file module in Julia

ExpectationMaximizationPCA.jl is a Julia rewrite of empca which provides Weighted Expectation Maximization PCA, an iterative method for solving PCA while properly weighting data.

Fit interpretable models. Explain blackbox machine learning.

Source code for website for Astro 497 Fall 2022

A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, …

Materials for Mini-Symposium on Julia in Astronomy & Astrophysics Research at JuliaCon 2022

Preheat your MCMC

Implementations of parallel tempering algorithms to augment samplers with tempering capabilities

Interface between Turing.jl and MonteCarloMeasurements.jl

Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.

[forked from NASA] Viewpoints (vp) is a visualization tool for exploring large, multidimensional data.

Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications