Greeners

High-performance econometrics in Rust — statsmodels-grade coverage, compiled-language speed, and Rust's type safety guarantees. Named in honor of William H. Greene.

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Why Greeners?

Every serious econometrics tool makes a trade-off:

Tool	Strength	Weakness
R (plm, AER, sandwich)	Deep econometrics, mature	Slow on large panels, no type safety
Python (statsmodels)	Broad coverage, readable	GIL, interpreter overhead, silent type errors
Julia (FixedEffectModels)	Fast	Small ecosystem, GC pauses
C++	Maximum speed	No econometrics library exists

Greeners fills the gap: the estimator coverage of statsmodels, the performance of compiled code, and Rust's guarantee that type errors and data races are caught at compile time — not at 2 AM in production.

No Python or R runtime. No system BLAS or LAPACK. Built on faer — pure-Rust linear algebra with performance competitive with OpenBLAS, and a single cargo add greeners to install.

Beyond parity, Greeners includes methods statsmodels does not: panel fixed and random effects, Arellano-Bond GMM, IV/2SLS, Difference-in-Differences, and automatic binary variable detection — all in a self-contained library with built-in DataFrame, formula parser, and estimators.

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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(())
}

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Installation

[dependencies]
greeners = "1.4"

No system dependencies required.

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What's covered

Linear models

Estimator	Module	Key features
OLS	ols	HC1–HC4, NeweyWest, Clustered, ClusteredTwoWay, StudentT/Normal inference
WLS	wls	Weighted least squares
FGLS	gls	Feasible GLS, Cochrane-Orcutt AR(1)
GLSAR	glsar	GLS with AR errors
IV/2SLS	iv	Instrumental variables, all covariance types
Quantile	quantile	IRLS with bootstrap SE, any quantile
RLM	rlm	M-estimators: Huber, Tukey, Hampel, Andrews

Generalized linear models

Estimator	Module	Key features
GLM	glm	Gaussian, Binomial, Poisson, Gamma, InverseGaussian, Tweedie, NegBin; 9 link functions
GLM-GAM	glmgam	B-spline basis with ridge penalty
Beta	beta_model	Beta regression for (0,1) outcomes

Discrete choice & count models

Estimator	Module	Key features
Logit / Probit	discrete	MLE, AME/MEM marginal effects
MNLogit	mnlogit	Multinomial logit
OrderedLogit / OrderedProbit	ordered	Ordered outcomes
Poisson	poisson	Count data
NegBin / NegBinP / GenPoisson	negbin	Overdispersed counts
ZIP / ZINB	zero_inflated	Zero-inflated models
ConditionalLogit / MNLogit / Poisson	conditional	Conditional fixed-effects

Panel data

Estimator	Module	Key features
Fixed Effects	panel	Within transformation, entity clustering
Random Effects	panel	Swamy-Arora GLS
Between	panel	Group-mean regression
Arellano-Bond	dynamic_panel	Difference GMM, Sargan test, AR tests
Panel Threshold	threshold	Hansen (1999), bootstrap CI

Time series

Estimator	Module	Key features
ARIMA / SARIMAX	arima	Hannan-Rissanen, exogenous regressors, forecast intervals
AutoReg / ARDL	autoreg	Autoregressive distributed lag
ETS	ets	Additive/multiplicative error, trend, seasonality; damped trend
VAR	var	IRF (Cholesky), FEVD, AIC/BIC
VECM	vecm	Johansen cointegration, rank selection
VARMA	varma	State-space MLE
SVAR	svar	Cholesky, short-run, long-run, sign restrictions
Markov Switching	markov, markov_autoreg	Regime switching, autoregressive
GARCH / EGARCH / GJR-GARCH	garch	Normal + Student-t, BFGS MLE

State space & decomposition

Estimator	Module	Key features
Kalman Filter / Smoother	statespace	Forward + RTS backward pass
Unobserved Components	unobserved_components	Local level, trends, seasonal, cycles
Dynamic Factor	dynamic_factor	DFM
MSTL	mstl	Multiple seasonal-trend decomposition
Classical decomposition	decomposition	Additive/multiplicative

System estimation

Estimator	Module	Key features
SUR	sur	Seemingly Unrelated Regressions (Zellner FGLS)
3SLS	three_sls	Three-Stage Least Squares

Mixed & multilevel models

Estimator	Module	Key features
MixedLM	mixed	REML, random intercepts/slopes
BayesMixedGLM	mixed	Bayesian mixed GLM (MCMC)
GEE	gee	Independence, exchangeable, AR(1), unstructured
NominalGEE / OrdinalGEE	gee	Categorical outcomes

Causal & robust methods

Estimator	Module	Key features
DiD	did	ATT, parallel trends test, event study
GMM	gmm	Optimal weighting, J-test
Bootstrap	bootstrap	Pairs, residual, block; hypothesis testing

Survival analysis

Estimator	Module	Key features
Cox PH	survival	Partial likelihood, concordance index
Kaplan-Meier	survival	Survival curves

Multivariate & nonparametric

Estimator	Module	Key features
PCA	multivariate	Principal components
Factor Analysis	multivariate	Varimax, Quartimax, Equamax rotations
MANOVA	multivariate	Multivariate ANOVA
Canonical Correlation	multivariate	CCA
KDE	nonparametric	Univariate + multivariate kernel density
Kernel Regression	nonparametric	Nadaraya-Watson
Lowess	nonparametric	Locally weighted regression

Rolling & recursive

Estimator	Module	Key features
RollingOLS / RollingWLS	rolling	Moving-window estimation
RecursiveLS	rolling	Expanding-window least squares

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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.

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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)

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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)

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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

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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.

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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

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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

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About the name

William H. Greene is the author of Econometric Analysis — the reference text used in graduate econometrics programs worldwide. Greeners is named in his honor: the goal is to make the methods he systematized available to anyone writing production systems in Rust.

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Roadmap

Active development targets full statsmodels parity. See ROADMAP.md for the complete feature matrix with implementation status.

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Contributing

Contributions are welcome. See CONTRIBUTING.md for guidelines.

Before opening a PR:

• Add at least one example to examples/ for new estimators
• Verify numerical output against statsmodels or R on a reference dataset
• Run cargo test and cargo clippy -- -D warnings

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License

MIT