Pymc is used in Python projects. Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with PyTensor It has 10 direct runtime dependencies. Check its dependency graph on PyDeps to understand the full transitive dependency tree, reverse dependents, known CVEs, and license compatibility before installing.
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with PyTensor
pymc declares 10 direct runtime dependencies on PyPI. Each one is resolved into the full dependency tree below:
Beyond its direct dependencies, pymc can pull in further packages through its dependency tree. PyDeps resolves the entire chain from PyPI and deps.dev so you can see every transitive (nested) dependency of pymc, expand any node on demand, and understand the full set of code that ships when you run pip install pymc.
PyDeps checks pymc and every package in its dependency tree against the OSV vulnerability database in real time. For each CVE you can see the severity, the affected version ranges, and the first fixed version, so you know exactly which pymc version is safe to install before you ship.
pymc is distributed under the Apache License, Version 2.0. PyDeps also shows the license of every dependency in the tree so you can audit license compatibility across your whole pymc install, not just the top-level package.
Install from PyPI with pip install pymc. For offline or air-gapped environments, PyDeps can download pymc together with every resolved dependency as wheel files in a single bundle, matched to your target Python version and operating system.
Switch to the dependents view to see the reverse dependencies of pymc — the PyPI packages that list pymc as a requirement. Reverse dependencies are a strong signal of how widely a package is trusted and how disruptive a breaking change would be.