Fast Berry–Levinsohn–Pakes (BLP) demand estimation in Rust

blprs (/ˈblu.pərz/) is a Rust implementation of pyBLP. The crate currently implements the demand side of random-coefficient logit models and is actively expanding toward full parity. The API tracks pyBLP concepts (problems, formulations, integrations, moments) so users can port notebooks and scripts with minimal friction. Supply-side estimation, optimal instruments, counterfactual engines, and other advanced features are actively under development.

Add the crate from crates.io:

cargo add blprs

or depend on the repository while the API stabilises:

[dependencies]
blprs = { git = "https://github.com/emaadmanzoor/blprs" }

use blprs::{ContractionOptions, Problem, ProblemOptions, WeightingMatrix};
use blprs::data::ProductDataBuilder;
use blprs::integration::SimulationDraws;
use nalgebra::{DMatrix, DVector};

let market_ids = vec!["m1".to_string(), "m1".to_string(), "m2".to_string()];
let shares = DVector::from_vec(vec![0.3, 0.2, 0.4]);
let x1 = DMatrix::from_row_slice(3, 2, &[1.0, 10.0, 1.0, 15.0, 1.0, 12.0]);
let x2 = DMatrix::from_row_slice(3, 1, &[10.0, 15.0, 12.0]);

let products = ProductDataBuilder::new(market_ids, shares)
    .x1(x1)
    .x2(x2)
    .build()
    .expect("validated product data");

let draws = SimulationDraws::standard_normal(200, 1, 1234);
let problem = Problem::builder()
    .products(products)
    .draws(draws)
    .options(ProblemOptions::default())
    .build()
    .unwrap();

let sigma = DMatrix::from_row_slice(1, 1, &[2.0]);
let options = ProblemOptions::default()
    .with_contraction(ContractionOptions { tolerance: 1e-10, ..Default::default() })
    .with_weighting(WeightingMatrix::InverseZTZ);

let results = problem.solve_with_options(&sigma, &options).unwrap();
println!("beta = {:?}", results.beta);

• R/pyBLP-style builder surface for configuring problems
• Validated product data with contiguous market partitioning
• Monte Carlo integration with reproducible seeds
• BLP contraction with configurable damping and diagnostics
• Two-step GMM estimator with customizable weighting matrices
• Rich error reporting for data shape issues and solver failures

Planned parity items include:

• Supply-side markups, marginal costs, and conduct alternatives
• Optimal instruments and demographic interactions
• Micro moment support and importance sampling
• Counterfactual engines (mergers, taxes, welfare analysis)
• Extended integration schemes (Halton, Sobol, sparse grids)
• Analytic gradients, clustered standard errors, and bootstrapping

The project roadmap tracks which pyBLP features have landed and what is in progress.

Feedback and pull requests are welcome. Please use the GitHub issue tracker for bugs or feature requests.

Development quickstart:

cargo fmt
cargo clippy
cargo test

Licensed under the MIT License. See LICENSE for details.