Reverse Dependencies of graphviz

The following projects have a declared dependency on graphviz:

• lib310 — lib310 Python Package
• libcirkit — a python framework to build, learn and reason about probabilistic circuits and tensor networks
• liblet — A teaching aid library for formal languages and compiler courses.
• light-deep-ai — Light AI tools developed by Gang Zhang
• lightning-flash — Your PyTorch AI Factory - Flash enables you to easily configure and run complex AI recipes.
• lightning-thunder — Lightning Thunder project.
• lineapy — Data engineering, simplified. LineaPy creates a frictionless path for taking your data science artifact from development to production.
• lingam — LiNGAM Python Package
• linkml — Linked Open Data Modeling Language
• linuxns-rel — Linux namespace relationships library
• littledarwin — LittleDarwin Mutation Analysis Framework
• Livenodes — LiveNodes: node based live streaming sensor/data and visualization suite.
• LK-Info-Flow — Liang Information Flow Package
• llm-mri — Package to visualize LLM's Neural Networks activation regions
• logo-diagram-generator — Generate SVG diagrams of a (tech) ecosystem, using logos from each tool organised into groups around a central logo
• logtalk-jupyter-kernel — Hercutalk - A Jupyter Kernel for Logtalk
• lohrasb — This versatile tool streamlines hyperparameter optimization in machine learning workflows.It supports a wide range of search methods, from GridSearchCV and RandomizedSearchCVto advanced techniques like OptunaSearchCV, Ray Tune, and Scikit-Learn Tune.Designed to enhance model performance and efficiency, it's suitable for tasks of any scale.
• lolipop — A set of scripts to cluster mutational trajectories into genotypes and infer lineage in the context of population evolution experiments.
• LongTermBiosignals — Python library for easy managing and processing of large Long-Term Biosignals.
• lrmatrix — Logic Rules Matrix package to support the ExMatrix and VAX methods.
• lsha — Automata Learning Algorithm for Stochastic Hybrid Automata
• LUBEAT — This package contains several methods for calculating Conditional Average Treatment Effects
• luigi-tools — Tools to work with luigi.
• lux-explainer — Universal Local Rule-based Explainer
• LZBEAT — This package contains several methods for calculating Conditional Average Treatment Effects
• m4-utils — Biblioteca com funções de uso comum em projetos de aprendizado de máquina e ciencia de dados.
• mach2 — Migration Analysis of Clonal History 2
• machine-learning-tools — Utility functions for sklearn and other machine learning utilities
• macq — Action model acquisition from state trace data.
• magma-lang — An embedded DSL for constructing hardware circuits
• majora — Majora is a python library that automates common tasks in your exploratory data analysis.
• makefile2dot — Create a graphviz graph of a Makefile.
• malloctag-tools — Utility to postprocess malloc-tag JSON snapshots
• mammut-py — Mammut framework is an open library for computational linguistics.
• MAPSDT — MAPS Decision Tree
• maquinas — Formal languages and automata library
• mara-db — Configuration and monitoring of database connections
• mara-pipelines — Opinionated lightweight ELT pipeline framework
• mara-schema — Mapping of DWH database tables to business entities, attributes & metrics in Python, with automatic creation of flattened tables
• maraudersmap — Fast generation of Inports graph
• markov-solver — Utility to solve Markov chains.
• matchpy — A library for pattern matching on symbolic expressions.
• math-spec-mapping — A library for easy mapping of mathematical specifications.
• maxframe — MaxFrame operator-based data analyze framework
• mayan-edms — Free Open Source Electronic Document Management System
• mcda — Package for Multi Criteria Decision Analysis
• mchmm — Markov chains and Hidden Markov models
• mddrt — Package for Multi-Dimension Directly Rooted Trees visualization
• mdml — Application of Deep learning on molecular dymanamics trajectories
• mealy — Model Error Analysis python package
• mechanism-learn — Mechanism-learn is a simple method which uses front-door causal bootstrapping to deconfound observational data such that any appropriate machine learning model is forced to learn predictive relationships between effects and their causes (reverse causal inference), despite the potential presence of multiple unknown and unmeasured confounding. The library is compatible with most existing ML deployments such as models built with Scikit-learn and Keras.
• melpoi — no summary
• memonto — no summary
• memory-graph — Draws a graph of your data to analyze its structure.
• merlinxai — MERLIN
• mesmerize — Calcium imaging analysis platform
• mesoscopy — Analysis pipeline for rodent widefield calcium imaging data.
• metabeyond — Metadata for Python
• metagraph — Graph algorithm solver across multiple hardware backends
• metapipeline — test multiple parameters for complexe pipeline
• metient — no summary
• metricflow — Translates a simple metric definition into reusable SQL and executes it against the SQL engine of your choice.
• metricflow-lite — Lite version of MetricFlow. This version is missing dependencies for using connectors with data warehouses
• mf-manifest-tools-simple — MF Manifest Tools Simple
• microdegree-management — no summary
• micrograd2023 — micrograd2023 was developed based on Andrej Karpathy micrograd with added documentations using nbdev for teachning purposes
• micronets — A simple autograd engine along with a PyTorch/Tensorflow like API module to easily train neural nets
• MicroRegEx — A micro regular expression engine
• mig-meow — MiG based manager for event oriented workflows
• mikado-graph — Python tool to draw graphs for Mikado refactoring
• milligrad — Basic neural network library
• minigrad-python — A minimal deep learning framework with automatic differentiation
• minimata — Minimata is a minimalist state-machine implementation.
• miniwdl — Workflow Description Language (WDL) local runner & developer toolkit
• misso — Python package to compute mutual information matrix
• MiV-OS — Python software for analysis and computing framework used in MiV project.
• mkdocs-juvix-plugin — MkDocs documentation with support for Juvix Markdown files
• ml-express — A Python library for day to day data analysis and machine learning.
• ML-Navigator — ML-Navigator is a tutorial-based Machine Learning framework. The main component of ML-Navigator is the flow. A flow is a collection of compact methods/functions that can be stuck together with guidance texts.
• ml4pd — ML4PD - an open-source libray for building Aspen-like process models via machine learning.
• mlblocks — Pipelines and primitives for machine learning and data science.
• mlconfound — Tools for analyzing and quantifying effects of counfounder variables on machine learning model predictions.
• mlduct — A personal framework for Machine Learning Pipelines.
• mlonmcu — This project contains research code related to the deployment of inferenceor learning applications on tiny micro-controllers.
• mlrun — Tracking and config of machine learning runs
• mlrun-pipelines-kfp-common — MLRun Pipelines package for providing KFP common functionality
• mmodel — Modular modeling framework for scientific modeling and prototyping.
• MNN-FMA — C methods for MNN Package
• modeci-mdf — ModECI (Model Exchange and Convergence Initiative) Model Description Format
• modelhub-sdk — SDK for ModelHub to create and manage machine learning pipelines
• modeling-gui — A PyQt5-based GUI for running statistical and machine learning models with customizable parameters and visualizations.
• moderne-visualizations-misc — Miscellaneous visualizations for the Moderne platform
• modlog — ('Set logger with logging/colorlog automatically',)
• mona — Calculation framework
• mondobrain — MondoBrain API wrapper
• moodools — Moodools: tools for Odoo modules development
• morai — A mortality viewer
• morphology-workflows — Workflows used for morphology processing.
• mosromgr — Python library for managing MOS running orders
• mpdfg — Package for multi perspective DFG visualization