# xinychen/awesome-latex-drawing

Drawing Bayesian networks, graphical models, tensors, technical frameworks, and illustrations in LaTeX.

Repository: https://github.com/xinychen/awesome-latex-drawing
Canonical: https://ross.abutalabs.com/products/awesome-latex-drawing
Homepage: https://spatiotemporal-data.github.io/awesome-latex-drawing/
Language: TeX
License: MIT
License Family: permissive
Topics: latex, machine-learning
Last push: 2025-05-26T00:13:06+00:00

## Health v2 (maintenance only)
Score: 43/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 23, release rhythm 35, longevity 100
- inputs: {"age_days": 2791, "days_push": 465, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2045, forks 191 (observed 2026-08-28T04:06:08.840955+00:00)

## What it is
A curated collection of 30+ LaTeX examples for drawing academic graphics such as Bayesian networks, graphical models, function plots, and machine learning framework diagrams. It serves as a learning resource with reproducible, code-driven figure examples runnable on Overleaf.

## Use cases
- draw bayesian networks in latex
- latex tikz examples for academic figures
- plot probability distribution functions with pgfplots
- create machine learning framework diagrams in latex
- learn latex graphics for research papers
- reproduce publication figures with latex code

## When to choose
- you need publication-quality academic figures with mathematical notation
- you want copy-paste LaTeX examples for graphical models or tensor diagrams
- you prefer code-driven, reproducible figure generation over GUI drawing tools

## When to avoid
- you need a general-purpose diagramming tool with drag-and-drop editing
- you want charts generated from data programmatically in Python or R
- you need non-LaTeX output formats like SVG or PNG directly

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-visualization, documentation, graphics
- domain: data-visualization, machine-learning, education, tutorials
- platform: cross-platform
- tags: latex, tikz, pgfplots, bayesian-networks, academic-figures, overleaf, awesome-list

## Member repositories
- xinychen/awesome-latex-drawing (main) score 43

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:08.840955+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:58:02.227395+00:00, confidence not recorded.
  - readme: https://github.com/xinychen/awesome-latex-drawing (fetched 2026-08-28T04:06:08.840955+00:00, sha 7c9df8f5dbf8)
  - homepage: https://spatiotemporal-data.github.io/awesome-latex-drawing/ (fetched 2026-08-29T10:38:06.386308+00:00, sha 19c9d6b9cfec)
- Data as of 2026-08-30T08:39:29.467469+00:00.