Greeners

High-performance econometrics in Rust.

A self-contained econometrics library with R/Python-style formulas, robust inference, and comprehensive coverage of cross-sectional, time series, panel data, and system estimation methods. No external statistical dependencies — DataFrame, formula parser, and all estimators are built in.

Installation

System dependencies

# Debian/Ubuntu
sudo apt-get install gfortran libopenblas-dev liblapack-dev pkg-config

# Fedora/RHEL
sudo dnf install gcc-gfortran openblas-devel lapack-devel pkg-config

# Arch
sudo pacman -S gcc-fortran openblas lapack base-devel

# macOS
brew install openblas lapack

Cargo.toml

[dependencies]
greeners = "1.4.0"
ndarray = "0.17"
ndarray-linalg = { version = "0.18", features = ["openblas-system"] }

Quick start

use greeners::{OLS, DataFrame, Formula, CovarianceType};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let df = DataFrame::from_csv("data.csv")?;
    let formula = Formula::parse("wage ~ education + experience + C(region)")?;
    let result = OLS::from_formula(&formula, &df, CovarianceType::HC3)?;
    println!("{}", result);
    Ok(())
}

What's covered

Linear models

Generalized linear models

Discrete choice & count models

Panel data

Time series

State space & decomposition

System estimation

Mixed & multilevel models

Causal & robust methods

Survival analysis

Multivariate & nonparametric

Rolling & recursive

Diagnostics & tests

Regression diagnostics (diagnostics)

Jarque-Bera, Breusch-Pagan, Durbin-Watson, VIF, condition number, leverage, Cook's distance, omnibus, Harvey-Collier, Anderson-Darling.

Specification tests (specification_tests)

White test, RESET, Breusch-Godfrey, Goldfeld-Quandt.

Influence & stability (influence)

Influence measures, CUSUM test.

Time series tests (timeseries)

ADF, KPSS, Phillips-Perron, Zivot-Andrews (structural break), Ljung-Box, ACF/PACF.

Model selection (model_selection)

AIC/BIC comparison, Akaike weights, panel diagnostics (BP LM, F-test, Hausman).

Statistical tests (stats)

One-way and two-way ANOVA, Tukey HSD, Bonferroni post-hoc, regression F-tests.

Multiple testing (multipletests)

Bonferroni, Holm, Hochberg, Hommel, Benjamini-Hochberg, Benjamini-Yekutieli.

Other (proportion, descrstatsw, hausman)

Proportion tests (one/two-sample, equivalence), weighted descriptive statistics, Hausman test.

Inference

All linear models support two inference distributions via .with_inference():

use greeners::InferenceType;

let result = OLS::from_formula(&formula, &df, CovarianceType::HC3)?;
// Default: Student's t (exact, finite-sample)
let result_z = result.with_inference(InferenceType::Normal)?;
// Normal/z (asymptotic, statsmodels-compatible)

Covariance types

CovarianceType::NonRobust           // Classical
CovarianceType::HC1                 // White (1980)
CovarianceType::HC2                 // Leverage-adjusted
CovarianceType::HC3                 // Jackknife (recommended for small samples)
CovarianceType::HC4                 // Cribari-Neto (2004)
CovarianceType::NeweyWest(lags)     // HAC
CovarianceType::Clustered(ids)      // One-way clustering
CovarianceType::ClusteredTwoWay(a, b) // Two-way clustering (Cameron-Gelbach-Miller)

Formula syntax

// Basic
"y ~ x1 + x2"                    // with intercept
"y ~ x1 + x2 - 1"                // no intercept

// Categoricals
"y ~ x1 + C(region)"             // auto dummy encoding (K-1)

// Transforms
"y ~ x + I(x^2)"                 // polynomial
"y ~ log(x1) + sqrt(x2)"        // functions

// Interactions
"y ~ x1 * x2"                    // full: x1 + x2 + x1:x2
"y ~ x1 : x2"                    // interaction only

// Splines
"y ~ bs(x, df=5)"                // B-splines

DataFrame

// Load from CSV, JSON, URL, or builder
let df = DataFrame::from_csv("data.csv")?;
let df = DataFrame::from_csv_url("https://example.com/data.csv")?;
let df = DataFrame::from_json("data.json")?;
let df = DataFrame::builder()
    .add_column("y", vec![1.0, 2.0, 3.0])
    .add_column("x", vec![4.0, 5.0, 6.0])
    .build()?;

// Missing data
let clean = df.dropna()?;
let filled = df.fillna("x", 0.0)?;
let forward = df.fillna_ffill("x")?;
let interp = df.interpolate("x")?;

// Time series ops
let lagged = df.lag("price", 1)?;
let diffed = df.diff("price", 1)?;
let returns = df.pct_change("price", 1)?;

Column types are auto-detected: Float, Int, Bool (including binary detection from any two-value column), DateTime, Categorical, String.

Imputation

use greeners::{MICE, BayesGaussMI};

let imputed = MICE::impute(&df, 10)?;           // Multiple imputation by chained equations
let imputed = BayesGaussMI::impute(&df, 10)?;   // Bayesian Gaussian MI

Examples

59 examples in examples/. Run any of them:

cargo run --example formula_example
cargo run --example marginal_effects
cargo run --example specification_tests
cargo run --example panel_model_selection

License

GPL-3.0-or-later