Skip to main content

mergeron: Python for analyzing merger enforcement policy

PyPI - Package Version PyPI - Python Version PyPI - Package status documentation CI

https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json https://www.mypy-lang.org/static/mypy_badge.svg https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json License: BSD-3-Clause

Visualize the sets of mergers falling within specified concentration and diversion-ratio thresholds. Analyze merger investigations data published by the U.S. Federal Trade Commission in various reports on extended merger investigations (Second Requests) during 1996 to 2011. Generate data under specified distributions of firm counts, market shares, price-cost margins, and prices, optionally imposing equilibrium conditions for Bertrand oligopoly with MNL demand and restrictions implied by statutory filing thresholds. Compute intrinsic enforcement rates or intrinsic clearance rates using generated data, with bounds for concentration; diversion ratio; gross upward pricing pressure (GUPPI); critical marginal cost reduction (CMCR); and illustrative price rise (IPR).

Documentation

An introductory usage guide and full API reference are available online.

Metadata

Release files for mergeron 2026.739867.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for mergeron 2026.739867.1
File Interpreter ABI Platform
mergeron-2026.739867.1-py3-none-any.whl Python 3 none any Details

Release files / mergeron-2026.739867.1-py3-none-any.whl

Download URL mergeron-2026.739867.1-py3-none-any.whl
Size 259.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
c8a606bb69db526fbfa4ee5f0192cc2a237f4c622d22f1a6d3e8f8f7bb2fa164
BLAKE2b-256 checksum
How to use checksums
3797fdb2adf2ce28639afee84c1fb245a3e39c20beb970cebcf35bb998d79f5d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via uv/0.12.4 {"installer":{"name":"uv","version":"0.12.4","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 14, 2026.

Transparency log
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page