# Tools Catalog — Automated Empirical Research & Causal Inference

<!-- GENERATED by scripts/build-tools-catalog.py from tools/tools.json — do not edit by hand. -->

Curated, license- and maintenance-aware index of **software tools** for automated empirical research and causal inference — distinct from the agent **skills** under [`../skills/`](../skills/). Source of truth: [`tools.json`](tools.json). Rebuild with `python3 scripts/build-tools-catalog.py`.

## Summary

**334 tools** across 6 categories.

| Category | Count |
|---|---:|
| Causal-inference & treatment-effect libraries | 31 |
| Econometrics & quasi-experimental libraries | 170 |
| Causal discovery / structure learning | 25 |
| Autonomous research & data-science agents | 51 |
| MCP servers (data & stats execution) | 48 |
| Benchmarks & datasets | 9 |

| By language | Tools | | By maintenance | Tools | | By license | Tools |
|---|---:|:-:|---|---:|:-:|---|---:|
| Python | 164 |  | 🟢 active | 181 |  | permissive (MIT/BSD/Apache/…) | 183 |
| R | 109 |  | 🟡 maintained | 94 |  | copyleft (GPL/AGPL/LGPL/CeCILL) | 104 |
| Stata | 53 |  | 🔴 dormant | 59 |  | unverified / unmapped | 39 |
| TypeScript | 16 |  |  |  |  | proprietary / non-OSI / custom | 8 |
| Julia | 11 |  |  |  |  |  |  |
| C++ | 6 |  |  |  |  |  |  |
| Java | 3 |  |  |  |  |  |  |
| JavaScript | 3 |  |  |  |  |  |  |

> `last_activity` and `stars_approx` are point-in-time snapshots from the curation pass (see [`README.md`](README.md) for caveats). Status: 🟢 active ≈ commit within ~6 months · 🟡 maintained ≈ within ~2 years · 🔴 dormant ≈ older.

## Causal-inference & treatment-effect libraries (31)

| Tool | Lang | License | Status | What it does |
|---|---|---|---|---|
| [Ananke](https://gitlab.com/causal/ananke) | Python | Apache-2.0 | 🟡 maintained · 2023-12 | Python package for causal inference using graphical models (DAGs, ADMGs, chain graphs) supporting nonparametric identification and semiparametric estimation under unmeasured confounding. |
| [bartCause](https://github.com/vdorie/bartCause) | R | GPL-2.0-or-later | 🟢 active · 2025-12 | R package for causal inference using Bayesian Additive Regression Trees (BART), fitting response/treatment models to estimate ATE/ATT/ITE. |
| [bcf](https://github.com/jaredsmurray/bcf) | R · C++ | unverified | 🟡 maintained · 2023-01 | Reference R implementation of Bayesian Causal Forests (Hahn-Murray-Carvalho) for heterogeneous treatment-effect estimation with regularized treatment-effect priors. |
| [CATENets](https://github.com/AliciaCurth/CATENets) | Python | BSD-3-Clause | 🟡 maintained · 2023-08 | sklearn-style JAX/PyTorch implementations of neural-network CATE estimators including TARNet, CFRNet, DragonNet, SNet, FlexTENet, and NN meta-learners. |
| [causal-curve](https://github.com/ronikobrosly/causal-curve) | Python | MIT | 🟡 maintained · 2024-05 | Python package for estimating causal dose-response curves (continuous-treatment effects) from observational data with confidence intervals. |
| [CausalImpact](https://github.com/google/CausalImpact) | R | Apache-2.0 | 🟢 active · 2026-03 | Google's R package estimating the causal effect of an intervention on a time series using a Bayesian structural time-series counterfactual model. |
| [Causalinference](https://github.com/laurencium/Causalinference) | Python | BSD-3-Clause | 🟡 maintained · 2025-06 | Classic Python package for treatment-effect estimation via propensity-score estimation, trimming, subclassification, matching, weighting, and least-squares. |
| [causallib](https://github.com/BiomedSciAI/causallib) | Python | Apache-2.0 | 🟢 active · 2026-05 | IBM's scikit-learn-style package for estimating causal effects from observational data via IPW, standardization, doubly-robust (AIPW), and matching estimators. |
| [CausalLift](https://github.com/Minyus/causallift) | Python | BSD-2-Clause | 🔴 dormant · 2019-08 | Uplift modeling package based on the T-learner targeting which customers to treat, usable with both A/B-test and observational data. |
| [CausalML](https://github.com/uber/causalml) | Python | Apache-2.0 | 🟢 active · 2026-05 | Uber's uplift modeling and causal ML toolkit providing CATE/ITE estimation via S/T/X/R meta-learners, uplift trees/forests, and tree-based treatment selection. |
| [CausalPy](https://github.com/pymc-labs/CausalPy) | Python | Apache-2.0 | 🟢 active · 2026-05 | PyMC Labs package for Bayesian (and OLS) causal inference in quasi-experimental designs including synthetic control, interrupted time series, difference-in-differences, regression discontinuity, and instrumental variables. |
| [causalToolbox](https://github.com/soerenkuenzel/causalToolbox) | R | GPL-3.0 | 🔴 dormant · 2021 | R toolbox for heterogeneous treatment effects implementing S/T/X/M/DR meta-learners with honest random forests and BART base learners (now mirrored at forestry-labs/causalToolbox). |
| [CausalTune](https://github.com/py-why/causaltune) | Python | Apache-2.0 | 🟡 maintained · 2024-12 | AutoML library for automated tuning and out-of-sample (energy-score) selection of causal estimators wrapping EconML/DoWhy via FLAML. |
| [DoubleML (Python)](https://github.com/DoubleML/doubleml-for-py) | Python | BSD-3-Clause | 🟢 active · 2026-05 | Object-oriented implementation of the double/debiased machine learning framework on top of scikit-learn for partially linear, IV, and interactive regression models. |
| [DoubleML (R)](https://github.com/DoubleML/doubleml-for-r) | R | MIT | 🟢 active · 2026-05 | R implementation of the double/debiased machine learning framework built on the mlr3 ecosystem for orthogonal-score estimation of treatment effects. |
| [DoWhy](https://github.com/py-why/dowhy) | Python | MIT | 🟢 active · 2025-11 | End-to-end Python causal inference library that models assumptions as a causal graph and provides a four-step identify/estimate/refute API with refutation-based robustness tests. |
| [EconML](https://github.com/py-why/EconML) | Python | MIT | 🟢 active · 2026-06 | Microsoft ALICE project package for estimating heterogeneous treatment effects (CATE) from observational data using double machine learning, orthogonal/causal forests, DRLearner, DeepIV and meta-learners. |
| [grf](https://github.com/grf-labs/grf) | R · C++ | GPL-3.0 | 🟢 active · 2026-04 | Generalized Random Forests for nonparametric heterogeneous treatment-effect estimation (causal forests), including IV, multi-arm, and survival forests with honest confidence intervals. |
| [ltmle](https://github.com/joshuaschwab/ltmle) | R | GPL-2.0 | 🟡 maintained · 2023-04 | R package for longitudinal targeted maximum likelihood estimation (and IPTW/G-computation) of treatment/censoring-specific mean outcomes and marginal structural models. |
| [MendelianRandomization](https://cran.r-project.org/web/packages/MendelianRandomization/index.html) | R | GPL-2.0-or-later | 🟢 active · 2024-04 | CRAN R package implementing many summary-data Mendelian randomization methods (IVW, MR-Egger, median, mode, contamination-mixture, cML, debiased IVW) for causal effect estimation. |
| [metalearners](https://github.com/Quantco/metalearners) | Python | BSD-3-Clause | 🟢 active · 2025-06 | QuantCo's library for CATE estimation with S/T/X/R/DR meta-learners featuring sound cross-fitting, multi-treatment support, and SHAP/optuna integrations. |
| [policytree](https://github.com/grf-labs/policytree) | R · C++ | MIT | 🟢 active · 2026-02 | R package learning optimal shallow decision-tree treatment policies via doubly-robust empirical welfare maximization using grf scores. |
| [pylift](https://github.com/wayfair/pylift) | Python | BSD-2-Clause | 🔴 dormant · 2022-11 | Wayfair's uplift modeling package implementing the Transformed Outcome method with uplift evaluation/visualization tools (repository archived). |
| [scikit-uplift](https://github.com/maks-sh/scikit-uplift) | Python | MIT | 🔴 dormant · 2022-08 | scikit-learn-style uplift modeling package providing solo-model/two-model/class-transformation approaches plus uplift metrics and visualizations. |
| [stochtree](https://github.com/StochasticTree/stochtree) | Python · R · C++ | MIT | 🟢 active · 2026-05 | Stochastic tree ensembles (BART/XBART/BCF) in R and Python for supervised learning and Bayesian Causal Forest treatment-effect estimation. |
| [tfcausalimpact](https://github.com/WillianFuks/tfcausalimpact) | Python | Apache-2.0 | 🟡 maintained · 2025-01 | Python port of Google's CausalImpact built on TensorFlow Probability for Bayesian structural time-series intervention analysis. |
| [tmle (R)](https://cran.r-project.org/web/packages/tmle/index.html) | R | BSD-3-Clause | 🟢 active · 2025-08 | Susan Gruber & van der Laan's R package for targeted maximum likelihood estimation of ATE/ATT/ATC for a binary point treatment with SuperLearner-based nuisance estimation. |
| [tmle3](https://github.com/tlverse/tmle3) | R | GPL-3.0 | 🔴 dormant · 2021-03 | Generalized targeted learning (TMLE) framework from the tlverse providing a unified interface for estimating a range of causal target parameters. |
| [TwoSampleMR](https://github.com/MRCIEU/TwoSampleMR) | R | MIT | 🟢 active · 2026-05 | R package for two-sample Mendelian randomization using GWAS summary data, interfacing the IEU OpenGWAS database with IVW, MR-Egger, median and mode estimators. |
| [UpliftML](https://github.com/bookingcom/upliftml) | Python | Apache-2.0 | 🔴 dormant · 2022-12 | Booking.com's scalable uplift modeling package with PySpark/H2O implementations of metalearners, uplift random forests, and retrospective/constrained estimation. |
| [zEpid](https://github.com/pzivich/zEpid) | Python | MIT | 🟡 maintained · 2022-10 | Epidemiology analysis package with causal inference estimators including IPTW/AIPW, g-formula (parametric and Monte Carlo), and TMLE. |

## Econometrics & quasi-experimental libraries (170)

| Tool | Lang | License | Status | What it does |
|---|---|---|---|---|
| [access](https://github.com/pysal/access) | Python | BSD-3-Clause | 🟢 active · 2025-12 | Classical and novel spatial accessibility-to-services measures (floating catchment, gravity, RAAM) within the PySAL ecosystem. |
| [admetan](https://ideas.repec.org/c/boc/bocode/s458561.html) | Stata | GPL-3.0-only | 🔴 dormant · 2019-02 | Stata module providing comprehensive aggregate-data meta-analysis and forest plots; deprecated since 2020 in favor of metan/ipdmetan. |
| [AER](https://cran.r-project.org/package=AER) | R | GPL-2.0-or-later | 🟢 active · 2026-02 | Applied Econometrics with R companion package providing IV (ivreg), tobit, count and other econometric estimators and datasets. |
| [allsynth](https://ideas.repec.org/c/boc/bocode/s459076.html) | Stata | GPL-3.0 | 🟡 maintained | Wrapper around synth automating bias-correction, in-space placebo inference and stacked multi-unit synthetic control (Wiltshire). |
| [anesrake](https://cran.r-project.org/package=anesrake) | R | GPL-2.0-or-later | 🔴 dormant · 2018-04 | Implements ANES-style iterative raking to weight survey data to known target population margins with automatic variable selection. |
| [ARDL](https://github.com/Natsiopoulos/ARDL) | R | GPL-3.0-only | 🟢 active · 2026-05 | Builds ARDL and unrestricted/restricted error-correction models and runs the Pesaran-Shin-Smith (2001) bounds test for cointegration. |
| [augsynth](https://github.com/ebenmichael/augsynth) | R | MIT | 🟡 maintained · 2024 | Augmented synthetic control method (and multisynth for staggered adoption) that de-biases SCM when pre-treatment fit is imperfect. |
| [AutoregressiveModels.jl](https://github.com/junyuan-chen/AutoregressiveModels.jl) | Julia | MIT | 🟡 maintained · 2024-04 | Julia toolkit for vector autoregressions with OLS estimation and structural impulse-response computation with bootstrap confidence bands. |
| [autumn](https://github.com/aaronrudkin/autumn) | R | MIT | 🟡 maintained · 2024-01 | Performs fast, tidy-friendly iterative proportional fitting (raking) to generate survey weights matching target population distributions. |
| [bacondecomp](https://cran.r-project.org/package=bacondecomp) | R | MIT | 🔴 dormant · 2020-01 | Goodman-Bacon decomposition of two-way fixed-effects DiD estimates into their underlying 2x2 comparison weights. |
| [balance](https://github.com/facebookresearch/balance) | Python | MIT | 🟢 active · 2026-06 | Workflow and methods (IPW, raking, post-stratification) for adjusting biased samples to infer about a target population. |
| [bayesmeta](https://cran.r-project.org/package=bayesmeta) | R | GPL-2.0-or-later | 🟡 maintained · 2025-08 | Bayesian random-effects meta-analysis and meta-regression, returning posterior and predictive distributions and shrinkage estimates. |
| [binscatter](https://github.com/michaelstepner/binscatter) | Stata | unverified | 🟡 maintained | Generates binned scatterplots to visualize conditional means / OLS relationships in Stata. |
| [binsreg](https://cran.r-project.org/package=binsreg) | R · Python · Stata | GPL-3.0 | 🟢 active · 2026-05 | Binscatter least-squares, quantile and GLM regression with valid confidence bands and shape-restriction tests. |
| [boottest](https://github.com/droodman/boottest) | Stata | GPL-3.0 | 🟢 active · 2026-04 | Fast wild bootstrap (null-imposed) and score bootstrap for cluster-robust inference with few clusters in Stata. |
| [brms](https://github.com/paul-buerkner/brms) | R | GPL-2.0-only | 🟢 active · 2026-06 | Fits Bayesian generalized (non-)linear multivariate multilevel models via Stan, widely used as the multilevel-regression engine for MRP. |
| [BVAR](https://github.com/nk027/bvar) | R | GPL-3.0-only | 🟡 maintained · 2024-02 | Hierarchical Bayesian VAR estimation with Giannone-Lenza-Primiceri conjugate-prior selection, computing impulse responses, forecasts and FEVD. |
| [bvarsv](https://cran.r-project.org/web/packages/bvarsv/index.html) | R | GPL-2.0-or-later | 🔴 dormant · 2015-10 | Bayesian estimation of a time-varying-parameter VAR with stochastic volatility (Primiceri 2005) for posterior predictive densities and impulse responses. |
| [cem](https://cran.r-project.org/package=cem) | R | GPL-2.0 | 🔴 dormant · 2022-09 | Coarsened exact matching for reducing imbalance between treatment and control groups in observational data. |
| [clubSandwich](https://cran.r-project.org/package=clubSandwich) | R | GPL-3.0 | 🟢 active · 2026-05 | Cluster-robust (CR2) variance estimators with small-sample corrections and Satterthwaite/Wald hypothesis tests. |
| [cobalt](https://cran.r-project.org/package=cobalt) | R | GPL-2.0-or-later | 🟢 active · 2026-05 | Balance tables and love-plots for samples preprocessed by matching, weighting or subclassification. |
| [coefplot](https://github.com/benjann/coefplot) | Stata | MIT | 🟢 active · 2025-08 | Plots coefficients/confidence intervals from estimation results or matrices (widely used for event-study graphs). |
| [compute.es](https://cran.r-project.org/package=compute.es) | R | GPL-2.0-or-later | 🟢 active · 2026-01 | Converts a wide range of test statistics into effect sizes (d, g, r, z', OR) with variances, CIs, and p-values for meta-analysis. |
| [csdid (Python)](https://github.com/d2cml-ai/csdid) | Python | MIT | 🟢 active · 2025 | Python port of the Callaway & Sant'Anna group-time ATT estimator for staggered DiD. |
| [csdid (Stata)](https://github.com/friosavila/stpackages/tree/main/csdid) | Stata | MIT | 🟢 active · 2025 | Stata implementation of Callaway & Sant'Anna group-time ATTs with panel and repeated cross-section support (Rios-Avila). |
| [csdid2](https://github.com/friosavila/stpackages/tree/main/csdid2) | Stata | MIT | 🟢 active · 2025 | Faster all-Mata reimplementation of csdid (Callaway-Sant'Anna staggered DiD) with extended functionality. |
| [designmatch](https://cran.r-project.org/package=designmatch) | R | GPL-2.0-or-later | 🟢 active · 2026-02 | Constructs matched samples that are balanced and representative by design via mixed-integer programming (cardinality/optimal matching). |
| [did](https://github.com/bcallaway11/did) | R | GPL-2.0 | 🟢 active · 2025-12 | Implements Callaway & Sant'Anna group-time average treatment effects for staggered difference-in-differences with multiple periods. |
| [did2s](https://github.com/kylebutts/did2s) | R | MIT | 🟢 active · 2026-03 | Two-stage difference-in-differences estimator (Gardner 2021) robust to heterogeneous treatment effects under staggered adoption. |
| [did_imputation](https://github.com/borusyak/did_imputation) | Stata | GPL-3.0 | 🟡 maintained · 2024 | Borusyak, Jaravel & Spiess imputation estimator and event-study plotting (did_imputation/event_plot) for staggered DiD in Stata. |
| [didimputation](https://github.com/kylebutts/didimputation) | R | MIT | 🟡 maintained · 2024 | Imputation-based DiD estimator of Borusyak, Jaravel & Spiess (2021/2024) for staggered treatment timing. |
| [DIDmultiplegt](https://github.com/chaisemartinPackages/did_multiplegt) | R · Stata | MIT | 🟢 active · 2026-02 | de Chaisemartin & D'Haultfoeuille heterogeneity-robust DiD estimators for multiple groups, periods and non-binary treatments (original version). |
| [DIDmultiplegtDYN](https://github.com/chaisemartinPackages/did_multiplegt_dyn) | R · Stata | MIT | 🟢 active · 2026-05 | Dynamic (event-study) heterogeneity-robust DiD estimator allowing treatments that switch on and off multiple times. |
| [differences](https://github.com/bernardodionisi/differences) | Python | GPL-3.0 | 🟢 active · 2026-04 | Difference-in-differences estimation in Python (Callaway-Sant'Anna and related estimators) for staggered adoption with heterogeneous effects. |
| [dmetar](https://github.com/MathiasHarrer/dmetar) | R | GPL-3.0-only | 🟡 maintained · 2025-05 | Companion package of helper functions for the 'Doing Meta-Analysis in R' guide, extending meta, metafor, and netmeta with diagnostics and visualizations. |
| [drdid (Stata)](https://github.com/friosavila/stpackages/tree/main/drdid) | Stata | MIT | 🟢 active · 2025 | Doubly-robust difference-in-differences estimators (Sant'Anna & Zhao 2020) for Stata; the building block for csdid. |
| [dynamac](https://github.com/andyphilips/dynamac) | R | GPL-2.0-or-later | 🔴 dormant · 2022-11 | Estimates single-equation ARDL/error-correction models, dynamically simulates and plots their responses, and tests for cointegration (Jordan & Philips). |
| [ebal](https://cran.r-project.org/package=ebal) | R | GPL-2.0-or-later | 🟢 active · 2026-04 | Entropy balancing reweighting so covariate moments match user-specified targets in observational studies (Hainmueller). |
| [Econometrics.jl](https://github.com/Nosferican/Econometrics.jl) | Julia | ISC | 🟡 maintained · 2024-12 | General econometrics package for Julia covering panel models, IV and discrete-choice estimators. |
| [esc](https://github.com/strengejacke/esc) | R | GPL-3.0-only | 🔴 dormant · 2023-09 | Computes effect sizes and their variances (d, g, r, OR, etc.) from diverse reported statistics for use in meta-analysis. |
| [esda](https://github.com/pysal/esda) | Python | BSD-3-Clause | 🟢 active · 2026-03 | Exploratory spatial data analysis: global and local autocorrelation (Moran's I, Geary, Getis-Ord, local Moran/LISA) for continuous and binary areal data. |
| [estimatr](https://cran.r-project.org/package=estimatr) | R | MIT | 🟡 maintained · 2025-02 | Fast design-based OLS/IV estimators (lm_robust, iv_robust, difference_in_means) with robust and cluster-robust standard errors. |
| [estout (esttab)](https://github.com/benjann/estout) | Stata | MIT | 🟢 active · 2026-04 | Produces publication-quality regression tables (esttab/estout) exportable to LaTeX, RTF, HTML and CSV. |
| [etwfe](https://github.com/grantmcdermott/etwfe) | R | MIT | 🟢 active · 2026-03 | Extended two-way fixed effects (Wooldridge) DiD via saturated cohort-by-time interactions plus marginal-effects aggregation. |
| [eventstudyinteract](https://github.com/lsun20/eventstudyinteract) | Stata | MIT | 🟡 maintained · 2023 | Sun & Abraham interaction-weighted event-study estimator robust to heterogeneous treatment effects under staggered timing. |
| [eventstudyr](https://cran.r-project.org/package=eventstudyr) | R | MIT | 🟢 active · 2026-04 | Estimates and plots linear panel event-study models following Freyaldenhoven et al., including sup-t bands and pre-trend tests. |
| [fect](https://github.com/xuyiqing/fect) | R | MIT | 🟢 active · 2026-05 | Counterfactual estimators for causal panel analysis (two-way FE, interactive fixed effects, matrix completion) with diagnostic tests. |
| [FixedEffectModels.jl](https://github.com/FixedEffects/FixedEffectModels.jl) | Julia | MIT | 🟢 active · 2026-04 | Estimates linear models with high-dimensional fixed effects and instrumental variables in Julia (reghdfe/fixest analog). |
| [fixest](https://github.com/lrberge/fixest) | R | GPL-3.0 | 🟢 active · 2026-05 | Fast and user-friendly estimation of OLS, GLM and IV models with multiple high-dimensional fixed effects, with built-in clustered/robust inference and event-study tooling. |
| [ftools](https://github.com/sergiocorreia/ftools) | Stata | MIT | 🟢 active · 2026-01 | Fast Mata-based data manipulation backend (collapse/merge/egen) that powers reghdfe and other Stata commands. |
| [fwildclusterboot](https://github.com/s3alfisc/fwildclusterboot) | R | GPL-3.0 | 🔴 dormant · 2023-07 | Fast wild cluster bootstrap inference for OLS/IV with few clusters (R port of boottest); archived on CRAN, source remains on GitHub. |
| [GeoDa](https://github.com/GeoDaCenter/geoda) | C++ | GPL-3.0-only | 🟢 active · 2025-09 | Cross-platform desktop GUI for exploratory spatial data analysis, LISA mapping, spatial weights and basic spatial regression on lattice data. |
| [giddy](https://github.com/pysal/giddy) | Python | BSD-3-Clause | 🟢 active · 2025-12 | Geospatial distribution dynamics: spatial Markov chains, rank/mobility and directional LISA analysis of longitudinal spatial data. |
| [GLFixedEffectModels.jl](https://github.com/jmboehm/GLFixedEffectModels.jl) | Julia | MIT | 🟢 active · 2026-03 | Estimates GLMs (logit, Poisson, etc.) with high-dimensional fixed effects in Julia (ppmlhdfe analog). |
| [gsynth](https://github.com/xuyiqing/gsynth) | R | MIT | 🟢 active · 2026-03 | Generalized synthetic control imputing counterfactuals via interactive fixed-effects models, supporting multiple treated units and staggered timing. |
| [gtools](https://github.com/mcaceresb/stata-gtools) | Stata | MIT | 🟡 maintained · 2024-06 | C-plugin accelerated versions of common Stata data commands (collapse, egen, reshape, pctile) used in large-panel workflows. |
| [HonestDiD](https://github.com/asheshrambachan/HonestDiD) | R | MIT | 🟢 active · 2026-04 | Robust inference and sensitivity analysis for DiD/event-study designs under relaxations of the parallel-trends assumption (Rambachan & Roth). |
| [ipdmetan](https://ideas.repec.org/c/boc/bocode/s457785.html) | Stata | GPL-3.0-only | 🔴 dormant · 2022-10 | Stata module for two-stage individual-participant-data meta-analysis with subgroup and forest-plot support. |
| [ipfn](https://github.com/Dirguis/ipfn) | Python | MIT | 🟡 maintained · 2024-05 | Implements N-dimensional iterative proportional fitting to adjust a data matrix so its margins match specified target totals. |
| [ipfraking](https://ideas.repec.org/c/boc/bocode/s458430.html) | Stata | GPL-3.0-only | 🔴 dormant · 2018-05 | Stata module performing iterative proportional fitting (raking) to calibrate complex survey weights to control totals with trimming and diagnostics. |
| [ivmodel](https://cran.r-project.org/package=ivmodel) | R | GPL-2.0 | 🔴 dormant · 2023-04 | IV estimation with weak-instrument-robust inference (AR, CLR), power and sensitivity analysis for a single endogenous regressor. |
| [ivreg](https://cran.r-project.org/package=ivreg) | R | GPL-2.0-or-later | 🟢 active · 2026-03 | Instrumental-variables (2SLS/2SM/2SMM) regression with weak-instrument and endogeneity diagnostics. |
| [ivreg2](https://ideas.repec.org/c/boc/bocode/s425401.html) | Stata | GPL-3.0 | 🟡 maintained · 2024-08 | Extended IV/2SLS/LIML/GMM estimation with weak-instrument and overidentification diagnostics (Baum, Schaffer & Stillman). |
| [ivreghdfe](https://github.com/sergiocorreia/ivreghdfe) | Stata | MIT | 🟢 active · 2025-12 | Combines ivreg2 and reghdfe to run IV/2SLS/GMM regressions with many high-dimensional fixed effects. |
| [kmatch](https://github.com/benjann/kmatch) | Stata | MIT | 🟢 active · 2026-02 | Multivariate-distance and propensity-score matching with entropy balancing, IPW, CEM and regression adjustment. |
| [lfe](https://cran.r-project.org/package=lfe) | R | Apache-2.0 | 🟡 maintained · 2025-02 | Estimates linear models with multiple high-dimensional group fixed effects (and IV) by transforming away factors before OLS. |
| [libpysal](https://github.com/pysal/libpysal) | Python | BSD-3-Clause | 🟢 active · 2026-01 | Core PySAL components: spatial weights construction, computational geometry, graphs, and I/O underpinning the spatial-econometrics stack. |
| [linearmodels](https://github.com/bashtage/linearmodels) | Python | NCSA | 🟢 active · 2025-10 | Panel (fixed/random effects), IV/2SLS-GMM, system and asset-pricing estimators missing from statsmodels. |
| [localprojections](https://github.com/suahjl/localprojections) | Python | MIT | 🔴 dormant · 2023-09 | Implements Jordà (2005) local-projection impulse responses for single-entity time series and panel data, including threshold/state-dependent variants. |
| [LocalProjections.jl](https://github.com/junyuan-chen/LocalProjections.jl) | Julia | MIT | 🟡 maintained · 2024-04 | Julia implementation of local-projection methods for impulse-response estimation, including lag-augmented and smoothed local projections. |
| [locproj](https://ideas.repec.org/c/boc/bocode/s459204.html) | Stata | GPL-3.0-only | 🟢 active · 2026-02 | Stata (SSC) command estimating linear and nonlinear local-projection IRFs for time-series and panel data, supporting IV and quantile-regression variants (Ugarte Ruiz). |
| [lpirfs](https://github.com/AdaemmerP/lpirfs) | R | GPL-2.0-or-later | 🟢 active · 2025-12 | Estimates linear and nonlinear (state-dependent) impulse responses via Jordà (2005) local projections for time-series and panel data, with identified-shock and IV options. |
| [marginaleffects](https://cran.r-project.org/package=marginaleffects) | R | GPL-3.0 | 🟢 active · 2026-02 | Computes predictions, marginal effects/slopes, comparisons and marginal means with delta-method or simulation inference for 100+ model classes. |
| [MatchIt](https://github.com/kosukeimai/MatchIt) | R | GPL-2.0-or-later | 🟢 active · 2025-05 | Unified interface to nearest-neighbor, optimal, full, genetic and coarsened-exact matching for covariate balance in observational studies. |
| [meta](https://github.com/guido-s/meta) | R | GPL-2.0 | 🟢 active · 2026-05 | Standard meta-analysis methods including fixed/random-effects models, meta-regression, bias tests, and forest/funnel plots. |
| [meta](https://www.stata.com/manuals/meta.pdf) | Stata | proprietary | 🟢 active · 2026-06 | Stata's built-in meta suite for fixed/random-effects meta-analysis, meta-regression, forest/funnel plots, and small-study-effect tests. |
| [metabias](https://ideas.repec.org/c/boc/bocode/s404901.html) | Stata | GPL-3.0-only | 🔴 dormant · 2010-12 | Stata module testing for small-study effects / funnel-plot asymmetry (Egger, Begg, Harbord tests) in meta-analysis. |
| [metafor](https://github.com/wviechtb/metafor) | R | GPL-2.0-or-later | 🟢 active · 2026-05 | Comprehensive R package for conducting meta-analyses, including effect-size computation, fixed/random/mixed-effects models, moderators, and forest/funnel plots. |
| [metan](https://ideas.repec.org/c/boc/bocode/s456798.html) | Stata | GPL-3.0-only | 🟡 maintained · 2024-07 | Comprehensive Stata module for fixed- and random-effects meta-analysis of binary, continuous, or generic effect estimates with flexible forest plots. |
| [metareg](https://ideas.repec.org/c/boc/bocode/s446201.html) | Stata | GPL-3.0-only | 🔴 dormant · 2009-01 | Stata module performing random-effects meta-regression on study-level summary data with permutation-test p-values. |
| [metaSEM](https://github.com/mikewlcheung/metasem) | R | GPL-2.0-or-later | 🟢 active · 2026-05 | Conducts meta-analysis via structural equation modeling (using OpenMx/lavaan), including fixed/random-effects and meta-analytic SEM on correlation matrices. |
| [mgwr](https://github.com/pysal/mgwr) | Python | BSD-3-Clause | 🟡 maintained · 2024-01 | Calibration, inference and prediction for (multiscale) geographically weighted regression across GLM families with model diagnostics. |
| [modelsummary](https://cran.r-project.org/package=modelsummary) | R | GPL-3.0 | 🟢 active · 2026-02 | Publication-quality regression and summary tables (and coefficient plots) for many model classes in multiple output formats. |
| [mvmeta](https://ideas.repec.org/c/boc/bocode/s456970.html) | Stata | GPL-3.0-only | 🔴 dormant · 2022-04 | Stata module for multivariate random-effects meta-analysis and meta-regression on point estimates, variances, and covariances. |
| [netmeta](https://github.com/guido-s/netmeta) | R | GPL-2.0-or-later | 🟢 active · 2026-05 | Frequentist network meta-analysis for simultaneously comparing multiple treatments across studies, with inconsistency assessment and network graphs. |
| [network](https://ideas.repec.org/c/boc/bocode/s458319.html) | Stata | GPL-3.0-only | 🔴 dormant · 2018-04 | Stata module for network (mixed-treatment-comparison) meta-analysis using contrast-based multivariate meta-regression with inconsistency checks. |
| [optmatch](https://cran.r-project.org/package=optmatch) | R | MIT | 🟡 maintained · 2024-09 | Optimal bipartite matching using minimum-cost flow for distance/propensity-score matched designs. |
| [outreg2](https://ideas.repec.org/c/boc/bocode/s456416.html) | Stata | unverified | 🔴 dormant · 2014-08 | Produces formatted regression-output tables for Word/Excel/LaTeX from Stata estimation results (Roy Wada). |
| [panelView](https://github.com/xuyiqing/panelView) | R | MIT | 🟡 maintained · 2024-06 | Visualizes treatment status, missingness and outcome dynamics for panel/DiD datasets. |
| [plm](https://cran.r-project.org/package=plm) | R | GPL-2.0-or-later | 🟢 active · 2025-11 | Comprehensive panel-data econometrics toolkit with fixed/random effects estimators, robust covariances and panel diagnostic tests. |
| [ppmlhdfe](https://github.com/sergiocorreia/ppmlhdfe) | Stata | MIT | 🟢 active · 2026-01 | Poisson pseudo-maximum-likelihood regression with multiple high-dimensional fixed effects and robust separation handling. |
| [PracTools](https://cran.r-project.org/package=PracTools) | R | GPL-3.0-only | 🟢 active · 2026-01 | Tools and datasets for designing complex survey samples, computing sample sizes, and constructing/weighting survey samples. |
| [pretrends](https://github.com/jonathandroth/pretrends) | R | MIT | 🟡 maintained · 2024 | Computes the power of pre-trends tests and visualizes detectable violations of parallel trends in event studies. |
| [psmatch2](https://ideas.repec.org/c/boc/bocode/s432001.html) | Stata | unverified | 🔴 dormant · 2018-02 | Mahalanobis and propensity-score matching with common-support graphing and covariate-imbalance testing (Leuven & Sianesi). |
| [psychmeta](https://github.com/psychmeta/psychmeta) | R | GPL-3.0-or-later | 🟡 maintained · 2024-06 | Psychometric meta-analysis toolkit for bare-bones and artifact-corrected meta-analysis of correlations and d-values (Hunter-Schmidt methods). |
| [puniform](https://github.com/RobbievanAert/puniform) | R | GPL-2.0-or-later | 🟢 active · 2025-12 | Publication-bias-correcting meta-analysis methods (p-uniform / p-uniform*) based on the distribution of conditional p-values. |
| [pyfixest](https://github.com/s3alfisc/pyfixest) | Python | MIT | 🟢 active · 2026-04 | Fast high-dimensional fixed-effects OLS/IV/Poisson regression in Python following fixest syntax, with clustered and wild-bootstrap inference. |
| [PyMARE](https://github.com/neurostuff/PyMARE) | Python | MIT | 🟡 maintained · 2025-04 | Python meta-analysis and regression engine providing mixed-effects meta-regression estimators and effect-size combination. |
| [pysal](https://github.com/pysal/pysal) | Python | BSD-3-Clause | 🟢 active · 2026-01 | Meta-package bundling the Python Spatial Analysis Library submodules (libpysal, esda, spreg, mgwr, giddy, etc.) for spatial analysis and econometrics. |
| [PySVAR](https://github.com/fangli-DX3906/PySVAR) | Python | unverified | 🟡 maintained · 2024-06 | Small Python package for SVAR estimation and impulse responses across recursive (Cholesky), sign-restriction and optimization-based identification schemes. |
| [pysyncon](https://github.com/sdfordham/pysyncon) | Python | MIT | 🟡 maintained · 2025-01 | Python implementation of classic, robust, augmented and penalized synthetic control plus synthetic DiD. |
| [pysynthdid](https://github.com/MasaAsami/pysynthdid) | Python | Apache-2.0 | 🔴 dormant · 2023 | Python implementation of the synthetic difference-in-differences (SDID) estimator. |
| [PythonMeta](https://pypi.org/project/PythonMeta/) | Python | GPL-3.0-only | 🔴 dormant · 2021-11 | Python module for meta-analysis in evidence-based-medicine systematic reviews, with fixed/random-effects pooling and forest/funnel plots. |
| [quantipy3](https://github.com/Quantipy/quantipy3) | Python | MIT | 🟢 active · 2026-04 | Python 3 survey-data processing and analysis toolkit handling multiple-choice data, metadata, and case weighting (including raking). |
| [rddensity](https://github.com/rdpackages/rddensity) | R · Python · Stata | GPL-3.0 | 🟢 active · 2025 | Manipulation (density-discontinuity) testing for RD designs using local polynomial density estimators (McCrary-style sorting test). |
| [rdlocrand](https://github.com/rdpackages/rdlocrand) | R · Python · Stata | GPL-3.0 | 🟢 active · 2026-05 | Local-randomization methods for estimation, inference and window selection in regression discontinuity designs. |
| [rdmulti](https://github.com/rdpackages/rdmulti) | R · Python · Stata | GPL-3.0 | 🟡 maintained · 2025 | RD estimation and inference with multiple cutoffs or multiple running variables/scores. |
| [rdpower](https://github.com/rdpackages/rdpower) | R · Python · Stata | GPL-3.0 | 🟡 maintained · 2025 | Power, sample-size and minimum-detectable-effect calculations for regression discontinuity designs. |
| [rdrobust](https://github.com/rdpackages/rdrobust) | R · Python · Stata | GPL-3.0 | 🟢 active · 2026-05 | Estimation, robust bias-corrected inference and plotting for sharp/fuzzy regression discontinuity designs via local polynomials. |
| [reghdfe](https://github.com/sergiocorreia/reghdfe) | Stata | MIT | 🟢 active · 2026-01 | Linear regression with multiple high-dimensional fixed effects and clustered/robust standard errors in Stata. |
| [RegressionTables.jl](https://github.com/jmboehm/RegressionTables.jl) | Julia | MIT | 🟢 active · 2025-10 | Generates publication-quality regression tables (esttab/stargazer analog) for Julia models. |
| [regsensitivity](https://github.com/mattmasten/regsensitivity) | Stata | MIT | 🟡 maintained | Regression sensitivity analysis (Masten & Poirier breakdown frontiers) quantifying robustness to omitted-variable bias. |
| [rgeoda](https://github.com/GeoDaCenter/rgeoda) | R | GPL-2.0-or-later | 🟢 active · 2026-02 | R interface to libgeoda/GeoDa for ESDA, LISA spatial autocorrelation, spatial clustering and regionalization. |
| [RoBMA](https://github.com/FBartos/RoBMA) | R | GPL-3.0-only | 🟢 active · 2026-06 | Robust Bayesian model-averaged meta-analysis that adjusts for publication bias via selection models and PET-PEESE ensembles. |
| [robumeta](https://github.com/zackfisher/robumeta) | R | GPL-2.0-only | 🔴 dormant · 2023-03 | Robust variance estimation (RVE) meta-regression with large- and small-sample estimators for dependent effect sizes without distributional assumptions. |
| [rstanarm](https://github.com/stan-dev/rstanarm) | R | GPL-3.0-only | 🟢 active · 2026-06 | Bayesian applied regression modeling with Stan using familiar R formula syntax, commonly used to fit the multilevel models in MRP. |
| [S2sls](https://cran.r-project.org/package=S2sls) | R | GPL-2.0-or-later | 🔴 dormant · 2016-08 | Minimal package fitting a spatial-lag instrumental-variable regression by spatial two-stage least squares. |
| [samplics](https://github.com/samplics-org/samplics) | Python | MIT | 🟢 active · 2026-03 | Design-based analysis of complex survey data covering sample selection, weighting/calibration, estimation, and small area estimation. |
| [sampling](https://cran.r-project.org/package=sampling) | R | GPL-2.0-or-later | 🟡 maintained · 2025-07 | Provides survey sampling selection algorithms and calibration/weight estimators including variance estimation for complex designs. |
| [sandwich](https://cran.r-project.org/package=sandwich) | R | GPL-2.0-or-later | 🟡 maintained · 2024-09 | Object-oriented model-robust covariance matrix estimators (HC, HAC, clustered, panel-corrected). |
| [scpi](https://github.com/nppackages/scpi) | R · Python · Stata | MIT | 🟢 active · 2025 | Estimation, prediction-interval inference and graphics for synthetic control (scest/scpi), including multiple treated units and staggered adoption. |
| [sdid](https://github.com/Daniel-Pailanir/sdid) | Stata | GPL-3.0 | 🟢 active · 2025 | Synthetic difference-in-differences estimation with inference and graphics for Stata (Arkhangelsky et al. 2021). |
| [sensemakr](https://github.com/carloscinelli/sensemakr) | R · Python · Stata | GPL-3.0 | 🟡 maintained · 2024-07 | Sensitivity analysis to unobserved confounders for OLS via robustness values and contour plots (Cinelli & Hazlett). |
| [spaMM](https://cran.r-project.org/package=spaMM) | R | CeCILL-2.0 | 🟢 active · 2026-04 | Fits mixed-effect models with spatially correlated random effects (geostatistical and Markov-random-field GLMMs) via Laplace/h-likelihood approximations. |
| [SpatialDependence.jl](https://github.com/javierbarbero/SpatialDependence.jl) | Julia | MIT | 🟢 active · 2025-12 | Julia package for spatial weights matrices, spatial-autocorrelation tests (global/local Moran, Geary, Getis-Ord, LISA) and choropleth ESDA. |
| [spatialEco](https://github.com/jeffreyevans/spatialEco) | R | GPL-3.0-only | 🟢 active · 2026-05 | Utilities for spatial data manipulation, sampling and modelling including autologistic models, spatial smoothing and landscape/point-pattern metrics. |
| [spatialreg](https://github.com/r-spatial/spatialreg) | R | GPL-2.0-only | 🟢 active · 2026-03 | Estimates spatial cross-sectional lattice/areal models (SAR, SEM, SAC, Durbin) by maximum likelihood, spatial 2SLS and GMM following Cliff-Ord and Kelejian-Prucha. |
| [spdep](https://github.com/r-spatial/spdep) | R | GPL-2.0-or-later | 🟢 active · 2026-05 | Builds spatial weights matrices from contiguities/distances and computes spatial-autocorrelation tests (Moran's I, Geary's C, Getis-Ord, local LISA). |
| [spglm](https://github.com/pysal/spglm) | Python | BSD-3-Clause | 🔴 dormant · 2023-10 | Sparse-compatible generalized linear models (Gaussian, Poisson, logistic) serving as the estimation base for PySAL's spint and GWR modules. |
| [sphet](https://github.com/gpiras/sphet) | R | GPL-2.0-only | 🟡 maintained · 2024-12 | Fits Cliff-Ord spatial autoregressive models with heteroskedastic innovations via GMM/IV, including spatial HAC standard errors. |
| [spint](https://github.com/pysal/spint) | Python | BSD-3-Clause | 🔴 dormant · 2020-09 | Calibrates gravity-type spatial interaction models (unconstrained and production/attraction-constrained Poisson) via entropy maximization. |
| [splm](https://cran.r-project.org/package=splm) | R | GPL-2.0-only | 🟡 maintained · 2023-12 | Maximum-likelihood and GM estimation plus diagnostic testing of fixed/random-effects econometric models for spatial panel data (Millo & Piras). |
| [spmoran](https://github.com/dmuraka/spmoran) | R | GPL-2.0-or-later | 🟡 maintained · 2024-12 | Estimates Moran-eigenvector spatial/spatio-temporal regression models with spatially varying coefficients for Gaussian and non-Gaussian data. |
| [sppack (spreg/spivreg/spmat)](https://ideas.repec.org/c/boc/bocode/s457245.html) | Stata | GPL-3.0-only | 🔴 dormant · 2018-12 | Community Stata (SSC) precursor to official Sp: builds spatial-weighting matrices (spmat) and fits SAR/SEM/SAC by ML and GS2SLS (spreg, spivreg) by Drukker, Peng, Prucha & Raciborski. |
| [spreg](https://github.com/pysal/spreg) | Python | BSD-3-Clause | 🟢 active · 2026-05 | PySAL spatial econometric regression: OLS/2SLS with spatial lag and error (SAR/SEM/SARAR/Durbin), GM/ML estimators, panel and regimes models. |
| [spsur](https://github.com/rominsal/spsur) | R | GPL-3.0-only | 🟢 active · 2025-09 | Tests and estimates spatial Seemingly Unrelated Regression (SUR-SLM/SEM/SDM/SLX) systems by maximum likelihood and three-stage least squares. |
| [sptotal](https://github.com/highamm/sptotal) | R | GPL-2.0-or-later | 🔴 dormant · 2023-09 | Finite-population block kriging to predict totals and weighted sums from spatially autocorrelated sample data (Ver Hoef 2008). |
| [sreweight](https://ideas.repec.org/c/boc/bocode/s457312.html) | Stata | GPL-3.0-only | 🔴 dormant · 2014-01 | Stata module that reweights survey microdata to external aggregate totals using Deville-Sarndal calibration methods. |
| [srvyr](https://github.com/gergness/srvyr) | R | GPL-2.0-or-later | 🟢 active · 2026-03 | Provides dplyr-like syntax for computing summary statistics on complex survey data by wrapping the survey package. |
| [stackedev](https://github.com/joshbleiberg/stackedev) | Stata | unverified | 🟡 maintained | Stacked event-study estimator (Cengiz et al.) that builds clean cohort-vs-never-treated stacks to avoid bad TWFE comparisons. |
| [staggered](https://github.com/jonathandroth/staggered) | R | unverified | 🟢 active · 2025-12 | Efficient estimators (Roth & Sant'Anna) for difference-in-differences settings with randomized/as-good-as-random treatment timing. |
| [Stata lpirf / ivlpirf](https://www.stata.com/manuals/tslpirf.pdf) | Stata | proprietary | 🟢 active · 2026-01 | Official Stata (18+) commands estimating Jordà local-projection impulse-response functions, with ivlpirf adding instrumental-variables identification. |
| [Stata sp (spregress/spxtregress/spivregress)](https://www.stata.com/manuals/sp.pdf) | Stata | proprietary | 🟢 active · 2026-01 | Official Stata Sp suite fitting cross-sectional and panel spatial autoregressive models (SAR/SEM/SAC, with endogenous covariates) by ML and GS2SLS. |
| [Stata var / svar / varbasic](https://www.stata.com/manuals/tsvar.pdf) | Stata | proprietary | 🟢 active · 2026-01 | Official Stata time-series suite estimating reduced-form and structural VARs (var, svar, varbasic) with IRF/FEVD via the irf subsystem. |
| [statsmodels](https://github.com/statsmodels/statsmodels) | Python | BSD-3-Clause | 🟢 active · 2025-12 | General-purpose statistical modeling library (OLS/GLM, robust/clustered SE, panel and time-series tools); a foundation rather than a quasi-experimental-specific package. |
| [survey](https://cran.r-project.org/package=survey) | R | GPL-2.0-or-later | 🟢 active · 2026-02 | Analysis of complex survey samples including design-based summary statistics, generalized linear models, calibration and raking of survey weights. |
| [svars](https://github.com/alexanderlange53/svars) | R | MIT | 🟡 maintained · 2025-10 | Data-driven identification of structural VARs (changes in volatility, GARCH, independent-component analysis, non-Gaussian ML) with IRFs and bootstrap inference. |
| [svy](https://www.stata.com/manuals/svy.pdf) | Stata | proprietary | 🟢 active · 2026-06 | Stata's built-in survey-data prefix and estimators that account for sampling weights, stratification, and clustering in complex survey designs. |
| [svyweight](https://github.com/mainwaringb/svyweight) | R | GPL-3.0-only | 🟢 active · 2026-03 | Quickly and flexibly applies rake weighting to survey data, extending the survey package's weighting interface to correct for non-response. |
| [Synth](https://cran.r-project.org/package=Synth) | R | GPL-2.0-or-later | 🟢 active · 2026-04 | Classic synthetic control method (Abadie, Diamond & Hainmueller) for comparative case studies with a single treated unit. |
| [synth](https://ideas.repec.org/c/boc/bocode/s457334.html) | Stata | unverified | 🟡 maintained | Original Stata implementation of the synthetic control method (Abadie, Diamond & Hainmueller). |
| [synth_runner](https://github.com/bquistorff/synth_runner) | Stata | unverified | 🔴 dormant · 2017-08 | Automates running synth across treated units/placebos to perform inference and produce synthetic-control plots. |
| [SynthControl.jl](https://github.com/nilshg/SynthControl.jl) | Julia | MIT | 🟡 maintained · 2024-02 | Pure-Julia synthetic control and synthetic difference-in-differences estimators (beta). |
| [synthdid](https://github.com/synth-inference/synthdid) | R | BSD-3-Clause | 🟡 maintained · 2024 | Reference R implementation of the synthetic difference-in-differences (SDID) estimator of Arkhangelsky et al. (2021). |
| [synthdid.py](https://github.com/d2cml-ai/synthdid.py) | Python | MIT | 🟡 maintained · 2025 | Python port of synthetic DiD supporting SDID/SC/DiD estimators with bootstrap, placebo and jackknife inference. |
| [SyntheticControlMethods](https://github.com/OscarEngelbrektson/SyntheticControlMethods) | Python | Apache-2.0 | 🔴 dormant · 2023 | Python package for classic and Differenced (robust) synthetic control estimation with placebo-based inference. |
| [tsDyn](https://github.com/MatthieuStigler/tsDyn) | R | GPL-2.0-or-later | 🟡 maintained · 2024-10 | Nonlinear and regime-switching time-series models including linear VAR/VECM and threshold TVAR/TVECM with associated cointegration tests. |
| [varexternalinstrument](https://github.com/angusmoore/varexternalinstrument) | R | MIT | 🔴 dormant · 2019-07 | Identifies VAR impulse responses using a high-frequency external instrument (proxy-SVAR / Gertler-Karadi), extending models fit with the vars package. |
| [vars](https://github.com/bpfaff/vars) | R | GPL-2.0-or-later | 🟡 maintained · 2024-03 | Estimation, lag selection, diagnostics, forecasting, Granger causality, IRFs and FEVD for VAR models plus SVAR and SVEC estimation (Pfaff). |
| [VARsignR](https://github.com/chrstdanne/VARsignR) | R | GPL-3.0-only | 🔴 dormant · 2015-12 | Identifies structural shocks in Bayesian VARs via sign restrictions (Uhlig rejection and penalty, Rubio-Ramirez QR, Fry-Pagan median target). |
| [Vcov.jl](https://github.com/FixedEffects/Vcov.jl) | Julia | unverified | 🟢 active · 2026-03 | Provides robust and clustered variance-covariance estimators as a backend for Julia regression packages. |
| [VectorAutoregressions.jl](https://github.com/lucabrugnolini/VectorAutoregressions.jl) | Julia | MIT | 🔴 dormant · 2022-06 | Julia VAR/BVAR/FAVAR estimation with IRF identification (Cholesky, long-run, sign restrictions) and asymptotic/bootstrap confidence bands. |
| [weakiv](https://github.com/kfinlay/weakiv) | Stata | unverified | 🔴 dormant | Weak-instrument-robust tests and confidence sets (AR, CLR, K) for IV/probit/tobit models (Finlay, Magnusson & Schaffer). |
| [weightipy](https://github.com/kaitumisuuringute-keskus/Weightipy) | Python | MIT | 🟢 active · 2026-02 | A modern, lightweight RIM (iterative raking) library for weighting survey/people data, a fork-style successor to quantipy's weighting. |
| [WeightIt](https://cran.r-project.org/package=WeightIt) | R | GPL-2.0-or-later | 🟢 active · 2026-04 | Generates balancing weights (propensity scores, entropy balancing, CBPS, energy balancing) for binary, multi-category and continuous treatments. |
| [weightr](https://cran.r-project.org/package=weightr) | R | GPL-2.0-or-later | 🔴 dormant · 2019-07 | Estimates the Vevea and Hedges (1995) weight-function model to assess and correct for publication bias in meta-analysis. |
| [weights](https://cran.r-project.org/package=weights) | R | GPL-2.0-or-later | 🟡 maintained · 2025-06 | Computes weighted descriptive statistics and tests (weighted correlations, t-tests, chi-squared) plus weighted graphics for survey data. |
| [wildboottest](https://github.com/s3alfisc/wildboottest) | Python | MIT | 🟡 maintained · 2024-08 | Fast wild cluster bootstrap algorithms for inference on OLS coefficients in Python. |
| [WildBootTests.jl](https://github.com/droodman/WildBootTests.jl) | Julia | unverified | 🟡 maintained | Julia engine for fast wild (cluster) bootstrap tests and confidence sets, used as the backend for boottest and fwildclusterboot. |
| [xsmle](https://ideas.repec.org/c/boc/bocode/s457610.html) | Stata | unverified | 🔴 dormant · 2017-01 | Stata (SSC) command estimating fixed/random-effects spatial panel models (SAR, SEM, Durbin, dynamic) by quasi-maximum likelihood with direct/indirect/total effects (Belotti, Hughes & Piano Mortari). |

## Causal discovery / structure learning (25)

| Tool | Lang | License | Status | What it does |
|---|---|---|---|---|
| [AVICI](https://github.com/larslorch/avici) | Python | MIT | 🟡 maintained · 2025-02 | Amortized variational inference for causal structure learning (NeurIPS 2022), predicting causal graphs directly from data via a trained neural network. |
| [benchpress](https://github.com/felixleopoldo/benchpress) | Python · R | GPL-2.0 | 🟢 active · 2026-05 | Snakemake workflow to run, develop and benchmark causal-discovery/structure-learning algorithms across many libraries (bnlearn, pcalg, causal-learn, gCastle, Tetrad, etc.) with data generators and metrics. |
| [bnlearn (Python)](https://github.com/erdogant/bnlearn) | Python | MIT | 🟢 active · 2026-03 | Independent Python package (built on pgmpy) for Bayesian network structure learning, parameter learning, inference and sampling. |
| [bnlearn (R)](https://cran.r-project.org/package=bnlearn) | R | GPL-2.0-or-later | 🟡 maintained · 2025-08 | Widely used R package for Bayesian network structure learning (constraint-based, score-based, hybrid), parameter learning and inference. |
| [Causal Discovery Toolbox (CDT)](https://github.com/FenTechSolutions/CausalDiscoveryToolbox) | Python | MIT | 🟡 maintained · 2025-10 | Python package for graph and pairwise causal discovery, bridging to R packages (pcalg, bnlearn) and providing deep-learning-based methods. |
| [causal-cmd](https://github.com/bd2kccd/causal-cmd) | Java | unverified | 🟢 active · 2026-03 | Command-line interface wrapping the Tetrad causal-discovery algorithms for running searches on data files from a shell. |
| [causal-learn](https://github.com/py-why/causal-learn) | Python | MIT | 🟢 active · 2026-06 | Comprehensive Python library of classic and state-of-the-art causal discovery algorithms (PC, FCI, GES, LiNGAM, Granger, etc.) for learning causal structure from observational data. |
| [causaldag](https://github.com/uhlerlab/causaldag) | Python | BSD-3-Clause | 🔴 dormant · 2023 | Python package for creating, manipulating and learning causal DAGs, including GSP/IGSP permutation-based and interventional structure-learning algorithms. |
| [CausalDisco](https://github.com/CausalDisco/CausalDisco) | Python | BSD-3-Clause | 🔴 dormant · 2023-11 | Python package of baseline causal-discovery algorithms and analytics tools (varsortability, sortnregress) for benchmarking structure learning. |
| [CausalNex](https://github.com/mckinsey/causalnex) | Python | Apache-2.0 | 🟡 maintained · 2024-06 | Python library for learning Bayesian network structure (NOTEARS-based) and reasoning about causal relationships for decision-making. |
| [causica](https://github.com/microsoft/causica) | Python | MIT | 🟡 maintained · 2024-12 | Microsoft's deep-learning library for end-to-end causal discovery and inference, including the DECI amortized causal-discovery model. |
| [DAGMA](https://github.com/kevinsbello/dagma) | Python | Apache-2.0 | 🟡 maintained · 2024-01 | Python package learning DAGs via continuous optimization using an M-matrix log-determinant acyclicity characterization (DAGMA). |
| [dodiscover](https://github.com/py-why/dodiscover) | Python | MIT | 🟢 active · 2026-05 | PyWhy's experimental causal discovery package providing constraint-based and other global structure-learning algorithms with a scikit-learn-style API. |
| [gCastle](https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle) | Python | Apache-2.0 | 🟢 active · 2026-06 | Python causal structure learning toolbox emphasizing gradient-based methods (NOTEARS, GraN-DAG, etc.) plus data simulators and SHD/F1 evaluation metrics. |
| [gimme](https://github.com/GatesLab/gimme) | R | GPL-2.0-or-later | 🟢 active · 2026-03 | R package (Group Iterative Multiple Model Estimation) that recovers group- and individual-level directed contemporaneous/lagged network structure from time series via unified SEM search. |
| [LiNGAM](https://github.com/cdt15/lingam) | Python | MIT | 🟢 active · 2026-05 | Python package implementing the LiNGAM family (ICA-LiNGAM, DirectLiNGAM, VAR-LiNGAM, RCD, etc.) for causal discovery in linear non-Gaussian models. |
| [NOTEARS](https://github.com/xunzheng/notears) | Python | Apache-2.0 | 🟢 active · 2026-05 | Reference implementation of NO TEARS, casting DAG structure learning as a continuous optimization with a smooth acyclicity constraint. |
| [pcalg](https://cran.r-project.org/package=pcalg) | R | GPL-2.0-or-later | 🟡 maintained · 2024-09 | Canonical R package for graphical-model causal structure learning (PC, FCI, RFCI, GIES) and causal effect estimation (IDA). |
| [pgmpy](https://github.com/pgmpy/pgmpy) | Python | MIT | 🟢 active · 2026-06 | Python toolkit for probabilistic graphical models with Bayesian network structure learning (PC, Hill-Climb, etc.), parameter learning, inference and causal reasoning. |
| [py-tetrad](https://github.com/cmu-phil/py-tetrad) | Python · Java | MIT | 🟢 active · 2026-05 | Python interface (via JPype) exposing the Java Tetrad causal-discovery algorithms in Python workflows. |
| [pyAgrum / aGrUM](https://gitlab.com/agrumery/aGrUM) | Python · C++ | LGPL-3.0-or-MIT | 🟢 active · 2026-01 | C++/Python library for probabilistic graphical models (Bayesian networks) with structure learning and causal do-calculus support. |
| [pywhy-graphs](https://github.com/py-why/pywhy-graphs) | Python | MIT | 🟢 active · 2026-05 | NetworkX-compliant causal graph data structures (ADMG, PAG, CPDAG) underpinning the PyWhy causal-discovery ecosystem. |
| [Tetrad](https://github.com/cmu-phil/tetrad) | Java | GPL-3.0 | 🟢 active · 2026-06 | Long-running Java toolkit and GUI for causal discovery and graphical-causal-model search, the reference implementation of many constraint- and score-based algorithms. |
| [tigramite](https://github.com/jakobrunge/tigramite) | Python | GPL-3.0 | 🟢 active · 2026-01 | Python package for causal discovery in time series via the PCMCI/PCMCI+/LPCMCI family of conditional-independence-based algorithms. |
| [typed-DAG (t-DAG)](https://github.com/ServiceNow/typed-DAG) | Python | Apache-2.0 | 🔴 dormant · 2023-07 | Reference implementation of causal discovery with typed directed acyclic graphs, integrating variable-type knowledge into structure learning. |

## Autonomous research & data-science agents (51)

| Tool | Lang | License | Status | What it does |
|---|---|---|---|---|
| [Agent Laboratory](https://github.com/SamuelSchmidgall/AgentLaboratory) | Python | MIT | 🟡 maintained · 2025-08 | End-to-end autonomous research workflow with literature-review, experimentation, and report-writing phases (and AgentRxiv shared-preprint collaboration) to turn a human research idea into a paper plus code. |
| [Agentic Data Scientist](https://github.com/K-Dense-AI/agentic-data-scientist) | Python | MIT | 🟢 active · 2026-05 | An adaptive multi-agent framework (Google ADK + Claude Agent SDK) that separates planning from execution with continuous validation to complete end-to-end data-science tasks. |
| [AI Data Science Team](https://github.com/business-science/ai-data-science-team) | Python | MIT | 🟢 active · 2025-12 | A library of specialized LLM agents (data cleaning, EDA, feature engineering, SQL, H2O AutoML, visualization) orchestrated by a supervisor to automate common data-science tasks. |
| [AI-Researcher](https://github.com/HKUDS/AI-Researcher) | Python | unverified | 🟡 maintained · 2025-10 | Fully autonomous research system (NeurIPS 2025) that runs the whole pipeline from literature review and idea generation through algorithm implementation to manuscript writing, primarily for AI/ML research. |
| [AIDE (aideml)](https://github.com/WecoAI/aideml) | Python | MIT | 🟢 active · 2026-05 | Tree-search ML-engineering agent that autonomously drafts, debugs, and benchmarks code to maximize a user-defined metric, reaching strong Kaggle/MLE-bench performance. (Overlaps with the data-science agent bucket.) |
| [Auto-Analyst](https://github.com/FireBird-Technologies/Auto-Analyst) | TypeScript · Python | MIT | 🟢 active · 2026-05 | A modular multi-agent AI data-scientist platform (DSPy-based) automating cleaning, statistical analysis, scikit-learn modeling, and Plotly visualization. |
| [Auto-Deep-Research](https://github.com/HKUDS/Auto-Deep-Research) | Python | MIT | 🟡 maintained · 2025-02 | A cost-efficient open Deep Research alternative (built on the AutoAgent framework) that autonomously gathers and synthesizes web information; strong on GAIA. |
| [AutoGluon Assistant (MLZero)](https://github.com/autogluon/autogluon-assistant) | Python | Apache-2.0 | 🟢 active · 2026-03 | A multi-agent system that transforms raw multimodal data (tabular, image, text, audio) into trained ML solutions end-to-end with zero human intervention, using MCTS-guided code generation over AutoGluon. |
| [AutoKaggle](https://github.com/multimodal-art-projection/AutoKaggle) | Python | Apache-2.0 | 🟡 maintained · 2024-12 | A multi-agent framework with five cooperating agents that autonomously complete Kaggle tabular competitions across six pipeline phases. |
| [AutoMind](https://github.com/innovatingAI/AutoMind) | Python | MIT | 🟢 active · 2025-10 | An adaptive, knowledge-grounded data-science agent using an expert knowledge base plus agentic tree search to build ML pipelines (beats AIDE on MLE-bench). |
| [AutoResearchClaw](https://github.com/aiming-lab/AutoResearchClaw) | Python | MIT | 🟢 active · 2026-06 | Self-reinforcing 23-stage autonomous research pipeline (literature discovery, multi-agent hypothesis debate, sandboxed self-healing experiments, peer review, LaTeX export) that turns an idea into a conference-ready paper. |
| [AutoSurvey](https://github.com/AutoSurveys/AutoSurvey) | Python | unverified | 🔴 dormant · 2025-02 | NeurIPS 2024 method that automatically writes comprehensive literature surveys via retrieval, parallel subsection drafting by specialized LLMs, and iterative refinement with automated evaluation. |
| [Aviary](https://github.com/Future-House/aviary) | Python | Apache-2.0 | 🟢 active · 2026-06 | Gymnasium/framework of language-agent environments for challenging scientific tasks (literature QA, DNA manipulation, protein engineering) used to build and train autonomous research agents. |
| [Biomni](https://github.com/snap-stanford/Biomni) | Python | Apache-2.0 | 🟢 active · 2025-10 | A general-purpose autonomous biomedical research agent combining LLM reasoning, retrieval-augmented planning, and code execution over a large library of biomedical tools. |
| [ChemCrow](https://github.com/ur-whitelab/chemcrow-public) | Python | MIT | 🔴 dormant · 2024-03 | An LLM agent augmented with chemistry tools (RDKit, paper-qa, reaction/retrosynthesis databases) that autonomously solves reasoning-intensive chemistry tasks. |
| [Coscientist](https://github.com/gomesgroup/coscientist) | Python | Apache-2.0 (Commons Clause) | 🔴 dormant | An LLM-driven autonomous lab agent (from the Nature paper) that plans, designs, and optimizes chemical experiments and synthesis. |
| [Curie](https://github.com/Just-Curieous/Curie) | Python | Apache-2.0 | 🟡 maintained · 2025-09 | AI agent framework for rigorous, automated scientific experimentation that handles the full hypothesis-to-analysis loop (experiment design, environment setup, execution, analysis) with reproducibility guarantees. |
| [CycleResearcher](https://github.com/zhu-minjun/Researcher) | Python | unverified | 🟢 active · 2026-03 | Open-source ecosystem of trained models (CycleResearcher + CycleReviewer) that iteratively generate research papers and improve them via automated peer review, focused on ML research. |
| [Data Formulator](https://github.com/microsoft/data-formulator) | TypeScript · Python | MIT | 🟢 active · 2026-05 | An AI tool with data-loading, exploration, and chart-style-refinement agents that transform and visualize data via a blend of UI interactions and natural language. |
| [Data-Copilot](https://github.com/zwq2018/Data-Copilot) | Python | MIT | 🔴 dormant · 2023 | An LLM agent that self-designs interface tools then dispatches them to autonomously query, process, analyze, and visualize (financial) data. |
| [data-to-paper](https://github.com/Technion-Kishony-lab/data-to-paper) | Python | MIT | 🟡 maintained · 2025-07 | Multi-agent system that goes from a raw dataset and research goal to a verifiable, data-traceable scientific paper, emphasizing reproducibility in data-driven (e.g. biomedical/clinical) research. |
| [DataMind](https://github.com/zjunlp/DataMind) | Python | Apache-2.0 | 🟢 active · 2026-06 | An open data-synthesis + agent-training recipe yielding generalist data-analytic LLMs (DataMind-7B/14B) that do multi-step, code-based reasoning over CSV/Excel/SQLite. |
| [deep-research (dzhng)](https://github.com/dzhng/deep-research) | TypeScript | MIT | 🟢 active · 2026-04 | A compact open-source deep-research agent that recursively searches, scrapes, and reasons over the web to produce reports, tracking goals across iterations. |
| [DeepAnalyze](https://github.com/ruc-datalab/DeepAnalyze) | Python | MIT | 🟢 active · 2026-03 | An agentic LLM (DeepAnalyze-8B) that autonomously runs the end-to-end data-science pipeline from raw structured/semi-structured/unstructured data to analyst-grade research reports. |
| [DeepEye](https://github.com/HKUSTDial/DeepEye) | Python · TypeScript | Apache-2.0 | 🟢 active · 2026-05 | A production-ready 'self-driving' data agent system that autonomously orchestrates multi-step workflows to produce dashboards, analytical reports, and data videos from heterogeneous data. |
| [DS-Agent](https://github.com/guosyjlu/DS-Agent) | Python | unverified | 🔴 dormant · 2024 | An ICML'24 data-science agent that uses case-based reasoning over Kaggle expert knowledge to iteratively build and train ML models across tabular/text/time-series. |
| [freephdlabor](https://github.com/ltjed/freephdlabor) | Python | MIT | 🟢 active · 2026-05 | Customizable multi-agent framework (ManagerAgent orchestrating Ideation/Experiment/Writeup agents) for building personalized systems that run continuous autonomous research toward publication-grade reports. |
| [GPT Researcher](https://github.com/assafelovic/gpt-researcher) | Python · TypeScript | Apache-2.0 | 🟢 active · 2026-05 | Autonomous deep-research agent that plans sub-questions, scrapes and aggregates many web/local sources, and synthesizes a long-form cited research report. (Also relevant to the data-science/deep-research bucket.) |
| [Jupyter AI](https://github.com/jupyterlab/jupyter-ai) | Python | BSD-3-Clause | 🟢 active · 2026-04 | A JupyterLab extension (v3) connecting agentic AI models to notebooks so they can read/write files, run code, and act via a built-in MCP server for data work. |
| [LIDA](https://github.com/microsoft/lida) | Python | MIT | 🔴 dormant · 2024-03 | An LLM agent that automatically summarizes data, generates analysis goals, and writes/executes/edits visualization code (treating viz as code) across grammars. |
| [MetaGPT (Data Interpreter / SELA)](https://github.com/geekan/MetaGPT) | Python | MIT | 🟢 active · 2026-01 | Multi-agent framework whose Data Interpreter (and SELA tree-search AutoML extension) agent plans, writes, and self-debugs code to solve data-analysis, ML, and modeling tasks. |
| [MLE-Agent](https://github.com/MLSysOps/MLE-agent) | Python | MIT | 🔴 dormant · 2024-10 | An AI companion that autonomously builds ML/AI baselines and end-to-end solutions (incl. Kaggle) with integrated arXiv/paper search. |
| [MLR-Copilot](https://github.com/du-nlp-lab/MLR-Copilot) | Python | unverified | 🟡 maintained · 2025-03 | Machine-learning research assistant framework where LLM agents autonomously generate research ideas from papers and implement/execute the corresponding experiments. |
| [Open Deep Research (LangChain)](https://github.com/langchain-ai/open_deep_research) | Python | MIT | 🟢 active · 2025-08 | A configurable, fully open-source deep-research agent (LangGraph-based) that works across many model/search providers; ranks on Deep Research Bench. |
| [Open Deep Research (nickscamara/Firecrawl)](https://github.com/nickscamara/open-deep-research) | TypeScript | Apache-2.0 | 🟡 maintained · 2025-02 | An open Deep Research clone that reasons over large amounts of web data extracted via Firecrawl to generate research analyses. |
| [Open Interpreter](https://github.com/OpenInterpreter/open-interpreter) | Python | AGPL-3.0 | 🔴 dormant · 2024-10 | A natural-language code-execution agent that runs Python/shell locally to plot, clean, and analyze datasets (and general computer tasks), with human approval of generated code. |
| [OpenResearcher](https://github.com/GAIR-NLP/OpenResearcher) | Python | Apache-2.0 | 🔴 dormant · 2024-10 | AI research-assistant platform that uses retrieval-augmented generation over scientific literature to autonomously answer research questions, summarize, and recommend papers with source citations. |
| [PandasAI](https://github.com/Sinaptik-AI/pandas-ai) | Python | MIT | 🟢 active · 2025-10 | A conversational data-analysis agent that turns natural-language questions over CSV/SQL/parquet data lakes into executed analysis code and charts. |
| [Paper2Code (PaperCoder)](https://github.com/going-doer/Paper2Code) | Python | Apache-2.0 | 🟢 active · 2026-03 | Multi-agent LLM system that autonomously converts an ML research paper into a faithful, runnable code repository via planning, analysis, and generation stages. |
| [PaperQA2](https://github.com/Future-House/paper-qa) | Python | Apache-2.0 | 🟢 active · 2026-03 | Agentic high-accuracy RAG system over full-text scientific literature that autonomously retrieves, ranks, and synthesizes cited answers and literature summaries with superhuman accuracy on QA/contradiction tasks. |
| [RD-Agent](https://github.com/microsoft/RD-Agent) | Python | MIT | 🟢 active · 2026-05 | Microsoft's R&D automation framework that iteratively proposes hypotheses and implements/evolves them as code, targeting data-driven R&D such as quantitative finance factor/model discovery and ML engineering. |
| [ResearchAgent](https://github.com/JinheonBaek/ResearchAgent) | Python | unverified | 🟡 maintained · 2025-08 | LLM system (NAACL 2025) that iteratively generates research problems, methods, and experiment designs grounded in an academic citation graph, refined by collaborating reviewing agents. |
| [Robin](https://github.com/Future-House/robin) | Python | Apache-2.0 | 🟢 active · 2026-04 | Multi-agent system (built on Aviary/PaperQA) that automates therapeutics discovery by generating hypotheses, proposing experiments, and analyzing experimental data, demonstrated by identifying a novel dry-AMD drug candidate. |
| [STORM / Co-STORM](https://github.com/stanford-oval/storm) | Python | MIT | 🟡 maintained · 2025-09 | LLM knowledge-curation system that researches a topic via multi-perspective simulated expert conversations and web search to autonomously synthesize a full, Wikipedia-style cited report (Co-STORM adds human-in-the-loop). |
| [SurveyX](https://github.com/IAAR-Shanghai/SurveyX) | Python | unverified | 🟡 maintained · 2026-01 | Academic survey-automation system that takes a title and keywords and autonomously retrieves literature and generates a structured, cited survey paper (open-source release is offline-only; full service is hosted). |
| [TableGPT Agent](https://github.com/tablegpt/tablegpt-agent) | Python | Apache-2.0 | 🟡 maintained · 2025-03 | A LangGraph-based pre-built agent for the TableGPT2 model that answers analytical questions and runs code over tabular datasets. |
| [TaskWeaver](https://github.com/microsoft/TaskWeaver) | Python | MIT | 🔴 dormant · 2026-03 | A code-first agent framework that plans and executes data-analytics tasks via generated Python, with stateful code/plugin memory (repo archived March 2026). |
| [The AI Scientist](https://github.com/SakanaAI/AI-Scientist) | Python | AI Scientist Source Code License v1.0 (custom, Responsible-AI based) | 🟡 maintained · 2025-12 | Fully automated pipeline that generates ML research ideas, writes and runs experiment code, and drafts complete LaTeX papers with an automated reviewer, in machine-learning domains. |
| [The AI Scientist-v2](https://github.com/SakanaAI/AI-Scientist-v2) | Python | AI Scientist Source Code License v1.0 (custom, Responsible-AI based) | 🟡 maintained · 2025-12 | Template-free successor to The AI Scientist that uses agentic tree search and an experiment-manager agent to autonomously produce workshop-level ML papers end-to-end. |
| [The Virtual Lab](https://github.com/zou-group/virtual-lab) | Python | MIT | 🟡 maintained · 2025-12 | Team of LLM agents (AI PI, domain researchers, scientific critic) that hold structured meetings to autonomously design scientific pipelines, demonstrated by designing new SARS-CoV-2 nanobodies. |
| [Virtual Scientists (VirSci)](https://github.com/InternScience/Virtual-Scientists) | Python | Apache-2.0 | 🟡 maintained · 2025-07 | Multi-agent 'science of science' system (ACL 2025) that simulates teams of scientist agents through team organization and inter/intra-team discussion to autonomously generate and evaluate novel research ideas. |

## MCP servers (data & stats execution) (48)

| Tool | Lang | License | Status | Data source / what it serves |
|---|---|---|---|---|
| [Academix](https://github.com/xingyulu23/Academix) | Python | MIT | 🟢 active · 2026-02 | Aggregator: OpenAlex, DBLP, Semantic Scholar, arXiv, Crossref |
| [akshare-one MCP](https://github.com/zwldarren/akshare-one-mcp) | Python | MIT | 🟢 active · 2026-03 | AKShare (Chinese stock market data) |
| [Alpha Vantage MCP (calvernaz)](https://github.com/calvernaz/alphavantage) | Python | Apache-2.0 | 🟢 active · 2026-02 | Alpha Vantage (stocks, FX, crypto) |
| [Alpha Vantage MCP Server (official)](https://github.com/alphavantage/alpha_vantage_mcp) | Python | MIT | 🟢 active · 2026-05 | Alpha Vantage (stocks, FX, crypto, fundamentals) |
| [ArXiv MCP Server](https://github.com/blazickjp/arxiv-mcp-server) | Python | Apache-2.0 | 🟢 active · 2026-05 | arXiv (preprints) |
| [BEA MCP Server (mcp-bea)](https://github.com/shawndrake2/mcp-bea) | TypeScript | unverified | 🟡 maintained · 2026-01 | BEA (US Bureau of Economic Analysis, GDP/income) |
| [bioRxiv MCP Server](https://github.com/JackKuo666/bioRxiv-MCP-Server) | Python | unverified | 🟡 maintained · 2025-03 | bioRxiv (biology preprints) |
| [BLS Labor MCP Server](https://github.com/cyanheads/bls-labor-mcp-server) | TypeScript | NOASSERTION | 🟢 active · 2026-06 | BLS (US Bureau of Labor Statistics) |
| [Crossref MCP Server (JackKuo666)](https://github.com/JackKuo666/Crossref-MCP-Server) | Python | unverified | 🟡 maintained · 2025-04 | Crossref (DOI metadata, 150M+ works) |
| [Data Commons Agent Toolkit (official MCP)](https://github.com/datacommonsorg/agent-toolkit) | Python | Apache-2.0 | 🟢 active · 2026-06 | Google Data Commons (unified public datasets) |
| [Data.gov MCP Server](https://github.com/melaodoidao/datagov-mcp-server) | JavaScript | MIT | 🟡 maintained · 2025-04 | Data.gov (US government open data catalog) |
| [doi-mcp (citation verifier)](https://github.com/tfscharff/doi-mcp) | TypeScript | unverified | 🟢 active · 2026-05 | Aggregator: Crossref, OpenAlex, etc. (citation verification by DOI) |
| [Eurostat MCP (ano-kuhanathan)](https://github.com/ano-kuhanathan/eurostat-mcp) | Python | MIT | 🟡 maintained · 2026-01 | Eurostat (EU official statistics) |
| [Eurostat MCP (dcerecedo)](https://github.com/dcerecedo/eurostat-mcp) | Python | NOASSERTION | 🟢 active · 2026-03 | Eurostat (EU official statistics) |
| [FinanceMCP (Tushare + Binance)](https://github.com/guangxiangdebizi/FinanceMCP) | JavaScript | MIT | 🟢 active · 2026-05 | Tushare (China A-shares, macro) + Binance (crypto) |
| [FRED MCP Server (stefanoamorelli)](https://github.com/stefanoamorelli/fred-mcp-server) | TypeScript | AGPL-3.0 | 🟢 active · 2026-05 | FRED (Federal Reserve Economic Data, 800k+ series) |
| [IMF Data MCP Server](https://github.com/c-cf/imf-data-mcp) | Python | Apache-2.0 | 🟢 active · 2026-04 | IMF (data.imf.org SDMX API) |
| [Jupyter MCP Server (Datalayer)](https://github.com/datalayer/jupyter-mcp-server) | Python | BSD-3-Clause | 🟢 active · 2026-05 | MCP server for Jupyter that lets an agent execute notebook cells and run Python/code in a live kernel with multimodal output. |
| [MCP-DBLP](https://github.com/szeider/mcp-dblp) | Python | MIT | 🟢 active · 2026-04 | DBLP (computer-science bibliography) |
| [mcp-fred (cfdude)](https://github.com/cfdude/mcp-fred) | Python | unverified | 🟢 active · 2026-03 | FRED (Federal Reserve Economic Data) |
| [mcp-stata (tmonk)](https://github.com/tmonk/mcp-stata) | Python | AGPL-3.0 | 🟢 active · 2026-05 | Lightweight Stata MCP server that executes commands, inspects data, retrieves stored r()/e() results, and views graphs in a chat interface. |
| [mcptools (Model Context Protocol for R)](https://github.com/posit-dev/mcptools) | R | NOASSERTION | 🟢 active · 2026-03 | Posit's official R package that turns a running R session into an MCP server (and client) so agents can execute R code and call R functions as tools. |
| [Nasdaq Data Link MCP Server](https://github.com/stefanoamorelli/nasdaq-data-link-mcp) | Python | MIT | 🟡 maintained · 2025-10 | Nasdaq Data Link / Quandl (alternative + financial time series) |
| [OECD MCP Server](https://github.com/isakskogstad/OECD-MCP) | TypeScript | MIT | 🟢 active · 2026-04 | OECD (SDMX, 5,000+ datasets) |
| [OpenAlex MCP (reetp14)](https://github.com/reetp14/openalex-mcp) | TypeScript | MIT | 🟡 maintained · 2025-07 | OpenAlex (scholarly works, authors, institutions) |
| [OpenAlex Research MCP](https://github.com/oksure/openalex-research-mcp) | JavaScript | MIT | 🟢 active · 2026-05 | OpenAlex (240M+ scholarly works) |
| [OpenEcon Data MCP Server](https://github.com/hanlulong/openecon-data) | Python | NOASSERTION | 🟢 active · 2026-05 | Aggregator: FRED, World Bank, IMF, Eurostat, BIS, UN Comtrade (330K indicators) |
| [Paper Search MCP](https://github.com/openags/paper-search-mcp) | Python | MIT | 🟢 active · 2026-05 | Aggregator: arXiv, PubMed, bioRxiv, Semantic Scholar, OpenAlex, Crossref, CORE, dblp, etc. |
| [paper-distill-mcp](https://github.com/Eclipse-Cj/paper-distill-mcp) | Python | AGPL-3.0 | 🟢 active · 2026-03 | Scholarly sources (paper search/curation) |
| [PubMed MCP Server (cyanheads)](https://github.com/cyanheads/pubmed-mcp-server) | TypeScript | Apache-2.0 | 🟢 active · 2026-06 | PubMed + Europe PMC + Unpaywall (biomedical literature/full text) |
| [PubMed MCP Server (JackKuo666)](https://github.com/JackKuo666/PubMed-MCP-Server) | Python | MIT | 🟡 maintained · 2025-05 | PubMed (35M+ biomedical citations) |
| [pubmed-search-mcp (u9401066)](https://github.com/u9401066/pubmed-search-mcp) | Python | NOASSERTION | 🟢 active · 2026-05 | Aggregator: PubMed, Europe PMC, CORE, OpenAlex (biomedical) |
| [RMCP (R MCP Server)](https://github.com/finite-sample/rmcp) | Python | MIT | 🟡 maintained · 2025-12 | MCP server exposing 50+ R statistical-analysis tools (regression, econometrics, time series, ML) backed by CRAN packages for AI agents. |
| [SEC EDGAR MCP](https://github.com/stefanoamorelli/sec-edgar-mcp) | Python | AGPL-3.0 | 🟢 active · 2026-05 | SEC EDGAR (US public-company filings, XBRL financials) |
| [Semantic Scholar MCP Server (JackKuo666)](https://github.com/JackKuo666/semanticscholar-MCP-Server) | Python | unverified | 🟡 maintained · 2025-03 | Semantic Scholar (200M+ papers, citations) |
| [Simple PubMed MCP (andybrandt)](https://github.com/andybrandt/mcp-simple-pubmed) | Python | MIT | 🟢 active · 2026-03 | PubMed (biomedical literature) |
| [Stata MCP (hanlulong)](https://github.com/hanlulong/stata-mcp) | Python | MIT | 🟢 active · 2026-04 | Stata MCP extension for VS Code, Cursor and Antigravity that executes Stata commands and do-files from an AI assistant. |
| [Stata MCP (SepineTam / mcp-for-stata)](https://github.com/SepineTam/stata-mcp) | Python | AGPL-3.0 | 🟢 active · 2026-06 | MCP server that lets an LLM agent write and run Stata regressions and econometric do-files for paper replication and hypothesis testing (repo renamed to mcp-for-stata). |
| [StatsPAI MCP Server](https://github.com/brycewang-stanford/StatsPAI) | Python | MIT | 🟢 active · 2026-06 | Agent-native causal inference and econometrics toolkit (DiD, IV, RDD, synth, DML, Bayesian, causal discovery) exposed as an MCP server with 900+ estimator tools. |
| [Tushare MCP (buuzzy)](https://github.com/buuzzy/tushare_MCP) | Python | MIT | 🟢 active · 2026-02 | Tushare (China A-shares financial data) |
| [Unpaywall MCP Server](https://github.com/ElliotPadfield/unpaywall-mcp) | TypeScript | MIT | 🟢 active · 2026-04 | Unpaywall (open-access full text by DOI) |
| [US Census Bureau Data API MCP (official)](https://github.com/uscensusbureau/us-census-bureau-data-api-mcp) | TypeScript | CC0-1.0 | 🟢 active · 2026-03 | US Census Bureau Data API (ACS, demographics) |
| [US Government Open Data MCP](https://github.com/lzinga/us-gov-open-data-mcp) | TypeScript | MIT | 🟢 active · 2026-04 | 40+ US government APIs (Treasury, FRED, Congress, FDA, CDC, FEC) |
| [World Bank Data360 MCP (official)](https://github.com/worldbank/data360-mcp) | Python | NOASSERTION | 🟢 active · 2026-06 | World Bank Data360 (development indicators, 200+ countries) |
| [World Bank Open Data MCP (anshumax)](https://github.com/anshumax/world_bank_mcp_server) | Python | unverified | 🟡 maintained · 2025-08 | World Bank Open Data API |
| [Yahoo Finance MCP (Alex2Yang97)](https://github.com/Alex2Yang97/yahoo-finance-mcp) | Python | MIT | 🟢 active · 2026-03 | Yahoo Finance (via yfinance) |
| [yfinance MCP (narumiruna)](https://github.com/narumiruna/yfinance-mcp) | Python | MIT | 🟢 active · 2026-06 | Yahoo Finance (via yfinance) |
| [Zotero MCP](https://github.com/54yyyu/zotero-mcp) | Python | MIT | 🟢 active · 2026-05 | Zotero (personal reference library) |

## Benchmarks & datasets (9)

| Tool | Lang | License | Status | What it does |
|---|---|---|---|---|
| [ACIC Competition data (aciccomp)](https://github.com/vdorie/aciccomp) | R | unverified | 🔴 dormant · 2020-07 | R packages with the data-generating processes and simulated datasets (with known ground-truth effects) from the 2016/2017 Atlantic Causal Inference Conference competitions. |
| [bnlearn Bayesian Network Repository](https://www.bnlearn.com/bnrepository/) | R | unverified | 🟡 maintained · 2025 | Curated collection of reference Bayesian networks (ASIA, ALARM, HEPAR2, etc.) with known ground-truth structure in multiple formats, the standard ground-truth benchmark for structure-learning evaluation. |
| [CausalBench](https://github.com/causalbench/causalbench) | Python | Apache-2.0 | 🟡 maintained · 2025-06 | GSK benchmark suite (with curated single-cell perturbation datasets) for evaluating network/causal-graph inference methods from gene-perturbation data. |
| [causaldata](https://github.com/NickCH-K/causaldata) | R · Python · Stata | unverified | 🟡 maintained · 2024-11 | R/Python/Stata packages providing the example datasets (LaLonde, NSW, etc.) used in The Effect, Causal Inference: The Mixtape, and What If textbooks. |
| [CEVAE datasets (IHDP)](https://github.com/AMLab-Amsterdam/CEVAE) | Python | unverified | 🔴 dormant · 2020-07 | Reference repo bundling the widely-cited IHDP (Infant Health and Development Program) semi-synthetic benchmark CSVs with known ground-truth treatment effects used across ITE papers. |
| [JustCause](https://github.com/inovex/justcause) | Python | MIT | 🔴 dormant · 2020-03 | Python framework providing standard causal-inference benchmark datasets (IHDP, IBM ACIC) plus synthetic-data generation and baseline comparison for evaluating ITE methods. |
| [RealCause](https://github.com/bradyneal/realcause) | Python | MIT | 🔴 dormant · 2021-03 | Realistic causal-inference benchmark that fits generative models to real data (LaLonde PSID/CPS, Twins) to produce samples with known ground-truth treatment effects. |
| [Tübingen Cause-Effect Pairs](https://webdav.tuebingen.mpg.de/cause-effect/) | — | unverified | 🟡 maintained · 2023 | Standard benchmark of ~100 bivariate cause-effect pairs with ground-truth causal direction for evaluating pairwise causal-discovery methods (Mooij et al. 2016). |
| [WhyNot](https://github.com/zykls/whynot) | Python | MIT | 🔴 dormant · 2020-06 | Python sandbox of dynamic simulators with known ground-truth causal effects for stress-testing causal-inference and sequential decision-making methods. |

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*Inclusion ≠ endorsement. Licenses/activity were verified during curation but change over time; confirm upstream before relying on a tool in a high-trust context. To propose a tool, see [`README.md`](README.md#contributing).*