Packages

csdid

Python adaptation of the R `did` package. Implements multi-period DiD with staggered treatment timing (Callaway & …

Python

bacondecomp

Performs Goodman-Bacon decomposition showing how two-way fixed effects (TWFE) estimates are weighted averages of all …

R

did

Implements group-time average treatment effects (ATT(g,t)) for staggered DiD designs with multiple periods and variation …

R

staggered

Provides the efficient estimator for randomized staggered rollout designs, offering optimal weighting schemes for …

R

Differences

Implements modern difference-in-differences methods for staggered adoption designs (e.g., Callaway & Sant'Anna).

Python

Provides rigorous prediction intervals for synthetic control methods following Cattaneo et al. (2021, 2025). Supports …

Extends synthetic control method to micro-level data with many units. Implements permutation inference and handles …

augsynth

Implements the Augmented Synthetic Control Method, which uses an outcome model (ridge regression by default) to correct …

R

Automates placebo tests and multi-treated-unit ATT calculations for synthetic control. Provides utilities for generating …

mlsynth

Implements advanced synthetic control methods: forward DiD, cluster SC, factor models, and proximal SC. Designed for …

Python

synthlearners

Fast synthetic control estimators for panel data problems. Optimized ATT estimation with multiple SC algorithms.

Python

GeoLift

Meta's geo-experimental methodology combining Augmented Synthetic Control with power analysis

R

gsynth

Implements generalized synthetic control with interactive fixed effects, extending SCM to multiple treated units with …

R

Brings synthetic control method into the tidyverse with cleaner syntax and built-in placebo inference. Provides …

synthdid

Implements synthetic difference-in-differences, a hybrid method combining insights from both DiD and synthetic control …

R

Changes‑in‑Changes (CiC) estimator for distributional treatment effects (Athey & Imbens 2006).

DRDID

Implements locally efficient doubly robust DiD estimators that combine inverse probability weighting and outcome …

R

trimmed_match

Google's robust analysis for paired geo experiments using trimmed statistics. Handles outliers in geo-level data.

Python

matched_markets

Google's time-based regression with greedy search for optimal geo experiment groups.

Python

didet

DiD with general treatment patterns. Handles effective treatment timing beyond simple staggered adoption.

Python

TFP CausalImpact

TensorFlow Probability port of Google's CausalImpact. Bayesian structural time-series for intervention effects.

Python

Lee (2009) sample‑selection bounds for treatment effects; trims treated distribution to match selection rates.

didhetero

Doubly robust estimation for group-time conditional average treatment effects. UCB for heterogeneous DiD.

Python

CausalImpact

Python port of Google's R package for estimating causal effects of interventions on time series using Bayesian …

Python

HonestDiD

Constructs robust confidence intervals for DiD and event-study designs under violations of parallel trends. Allows …

R

dcegm

JAX-compatible DC-EGM algorithm for discrete-continuous dynamic programming (Iskhakov et al. 2017).

Python

respy

Simulation and estimation of finite-horizon dynamic discrete choice (DDC) models (e.g., labor/education choice).

Python

Dolo

Framework for describing and solving economic models (DSGE, OLG, etc.) using a declarative YAML-based format.

Python

upper-envelope

Fast upper envelope scan for discrete-continuous dynamic programming. JAX and numba implementations.

Python

gEconpy

DSGE modeling tools inspired by R's gEcon. Automatic first-order condition derivation with Dynare export.

Python

pydsge

DSGE model simulation, filtering, and Bayesian estimation. Handles occasionally binding constraints.

Python

pynare

Python wrapper/interface to Dynare for DSGE model solving. Bridge between Python workflows and Dynare.

Python

econpizza

Solve nonlinear heterogeneous agent models (HANK) with perfect foresight. Efficient perturbation and projection methods.

Python

ruspy

Python package for simulation and estimation of Rust (1987) bus engine replacement model. Implements the nested fixed …

Python

QuantEcon.py

Core library for quantitative economics: dynamic programming, Markov chains, game theory, numerical methods.

Python

Greeners

Comprehensive Rust econometrics library with OLS, IV, panel data estimators, fixed effects, DiD, and …

Rust

Biogeme

Maximum likelihood estimation of parametric models, with strong support for complex discrete choice models.

Python

torch-choice

PyTorch framework for flexible estimation of complex discrete choice models, leveraging GPU acceleration.

Python

jaxonometrics

JAX-ecosystem implementations of standard econometrics routines for GPU computation.

Python

Argmin

Numerical optimization framework for Rust with Newton, BFGS, L-BFGS, trust region, and derivative-free methods for …

Rust

torchonometrics

Econometrics implementations in PyTorch. Leverages autodiff and GPU acceleration for econometric methods.

Python

gegravity

General equilibrium structural gravity modeling for trade policy analysis. Only Python package for Anderson-van Wincoop …

Python

Modern S4-based implementation of Generalized Method of Moments supporting systems of equations, nonlinear moment …

PyLogit

Flexible implementation of conditional/multinomial logit models with utilities for data preparation.

Python

OpenMx

Extended SEM software with programmatic model specification via paths (RAM) or matrix algebra, supporting mixture …

R

Implements Kleven-Waseem style bunching estimation for kink and notch designs. Calculates parametric elasticities from …

Implements modern treatment effect bounds beyond basic Manski worst-case scenarios. Provides tighter bounds using …

Handles both noncompliance AND network interference of unknown form following Hoshino and Yanagi (2023 JASA). Provides …

hdm

High-dimensional statistical methods featuring heteroscedasticity-robust LASSO with theoretically-grounded penalty …

R

rddensity

Implements manipulation testing (density discontinuity testing) procedures using local polynomial density estimators to …

R

Regression discontinuity design toolkit with clustered inference for geographic discontinuities. Provides bandwidth …

Companion package to 'Applied Econometrics with R' (Kleiber & Zeileis) plus datasets from Stock & Watson. Provides …

rdpower

Provides tools for power, sample size, and minimum detectable effects (MDE) calculations in RD designs using robust …

R

DoubleML

Implements the double/debiased ML framework (Chernozhukov et al.) for estimating causal parameters (ATE, LATE, POM) with …

Python

PySensemakr

Implements Cinelli-Hazlett framework for assessing robustness to unobserved confounding. Computes confounder strength …

Python

scikit-uplift

Focuses on uplift modeling and estimating heterogeneous treatment effects using various ML-based methods.

Python

pydtr

Dynamic treatment regimes using Iterative Q-Learning. Scikit-learn compatible for multi-stage optimal treatment …

Python

DoWhy

End-to-end framework for causal inference based on causal graphs (DAGs) and potential outcomes. Covers identification, …

Python

DoWhy

End-to-end framework for causal inference based on causal graphs (DAGs) and potential outcomes. Covers identification, …

Python

Linearmodels

Estimation of fixed, random, pooled OLS models for panel data. Also Fama-MacBeth and between/first-difference …

Python

FixedEffectModel

Panel data modeling with IV tests (weak IV, over-identification, endogeneity) and 2-step GMM estimation.

Python

bife

Estimates fixed effects binary choice models (logit and probit) with potentially many individual fixed effects using a …

R

plm

Comprehensive econometrics package for linear panel models providing fixed effects (within), random effects, between, …

R

pydynpd

Estimation of dynamic panel data models using Arellano-Bond (Difference GMM) and Blundell-Bond (System GMM). Includes …

Python

Fast estimation of linear models with multiple high-dimensional fixed effects (like R's `fixest`). Supports OLS, IV, …

Out-of-core regression (OLS/IV) for very large datasets using DuckDB aggregation. Handles data that doesn't fit in …

glmmTMB

Fit generalized linear mixed models with extensions including zero-inflation, hurdle models, heteroscedasticity, and …

R

Fit Gaussian linear and nonlinear mixed-effects models with flexible correlation structures, variance functions for …

causal-learn

Comprehensive Python package serving as Python translation and extension of Java-based Tetrad toolkit for causal …

Python

Ananke

Causal inference using graphical models (DAGs), including identification theory and effect estimation.

Python

MCD

Mixture of Causal Graphs discovery for heterogeneous time series (ICML 2024). Finds time-varying causal structures.

Python

Bayesian network structure learning, parameter estimation, and inference. Implements constraint-based (PC, GS), …

dagitty

Analysis of structural causal models represented as DAGs. Computes adjustment sets, identifies instrumental variables, …

R

causal-llm-bfs

LLM + BFS hybrid for efficient causal graph discovery. Uses language models to guide structure search.

Python

gCastle

Huawei Noah's Ark Lab end-to-end causal structure learning toolchain emphasizing gradient-based methods with GPU …

Python

SDCI

State-dependent causal inference for conditionally stationary processes (ICML 2025). Handles regime-switching causal …

Python

ggdag

Visualize and analyze causal DAGs using ggplot2. Provides tidy interface to dagitty with publication-quality DAG plots, …

R

Causal structure learning from observational data using the PC algorithm and variants. Estimates Markov equivalence …

Implements Propensity Score Matching (PSM) and Coarsened Exact Matching (CEM) with ML flexibility for propensity score …

Machine learning-based generalized propensity score estimation for continuous treatments. Uses SuperLearner ensemble …

Fast k-nearest-neighbor matching for large datasets using Facebook's FAISS library.

CausalInference

Implements classical causal inference methods like propensity score matching, inverse probability weighting, …

Python

MatchIt

Comprehensive matching package that selects matched samples of treated and control groups with similar covariate …

R

optmatch

Distance-based bipartite matching using minimum cost network flow algorithms, oriented to matching treatment and control …

R

Implements entropy balancing, a reweighting method that finds weights for control units such that specified covariate …

CBPS

Implements Covariate Balancing Propensity Score, which estimates propensity scores by jointly optimizing treatment …

R

WeightIt

Unified interface for generating balancing weights for causal effect estimation in observational studies. Supports …

R

cobalt

Generates standardized balance tables and plots for covariates after preprocessing via matching, weighting, or …

R

pyregadj

Regression and ML adjustments to treatment effects in RCTs. Implements List et al. (2024) methods.

Python

inferference

Computes inverse probability weighted (IPW) causal effects under partial interference following Tchetgen Tchetgen and …

R

EValue

Conducts sensitivity analyses for unmeasured confounding, selection bias, and measurement error in observational studies …

R

causalweight

Semiparametric causal inference methods based on inverse probability weighting and double machine learning for average …

R

ddml

Streamlined double/debiased machine learning estimation with emphasis on (short-)stacking to combine multiple base …

R

sensemakr

Suite of sensitivity analysis tools extending the traditional omitted variable bias framework, computing robustness …

R

Unified interface for six causal mediation approaches including traditional regression, inverse odds weighting, and …

Estimates Average Causal Mediation Effects (ACME) with sensitivity analysis for unmeasured confounding. Implements …

Principal stratification analysis for noncompliance and truncation-by-death using both Bayesian (Stan) and frequentist …

spilled_t

Treatment and spillover effect estimation under network interference. Separates direct and indirect effects.

Python

KECENI

Doubly robust, non-parametric estimation of node-wise counterfactual means under network interference (arXiv 2024).

Python

EconML

Microsoft toolkit for estimating heterogeneous treatment effects using DML, causal forests, meta-learners, and …

Python

CATENets

JAX-accelerated neural network CATE estimators implementing SNet, FlexTENet, TARNet, CFRNet, and DragonNet …

Python

mcf (Modified Causal Forest)

Comprehensive Python implementation for heterogeneous treatment effect estimation. Handles binary/multiple discrete …

Python

bartCause

Bayesian Additive Regression Trees for causal inference. Strong ACIC competition performer with sparsity-inducing priors …

R

CausalML

Focuses on uplift modeling and heterogeneous treatment effect estimation using machine learning techniques.

Python

grf

Forest-based statistical estimation and inference for heterogeneous treatment effects, supporting multiple treatment …

R

causalToolbox

Implements meta-learner algorithms (S-learner, T-learner, X-learner) for heterogeneous treatment effect estimation using …

R

Comprehensive package for dynamic treatment regimes implementing Q-learning, value search, and outcome-weighted learning …

Dynamic treatment regime estimation via G-estimation for sequential treatment decisions. Implements methods for finding …

aipyw

Minimal, fast AIPW (Augmented Inverse Probability Weighting) implementation for discrete treatments. Sklearn-compatible …

Python

causal-curve

Continuous treatment dose-response curve estimation. GPS and TMLE methods for continuous treatments.

Python

ltmle

Targeted maximum likelihood estimation for treatment/censoring-specific mean outcomes with time-varying treatments and …

R

SuperLearner

Implements the Super Learner algorithm for optimal ensemble prediction via cross-validation. Creates weighted …

R

Implements targeted maximum likelihood estimation for point treatment effects with binary or continuous outcomes. …

tmle3

A modular, extensible framework for targeted minimum loss-based estimation supporting custom TMLE parameters through a …

R

cowplot

Publication-ready ggplot2 themes and plot arrangement utilities. Provides clean themes, plot annotations, and functions …

R

patchwork

Compose multiple ggplot2 plots into publication-ready multi-panel figures. Uses intuitive operators (+, |, /) for …

R

see

Visualization toolbox for the easystats ecosystem built on ggplot2. Provides publication-ready plotting methods for …

R

Visual tool for exploring academic paper relationships. Creates visual graphs showing prior and derivative works.

Produces well-formatted LaTeX, HTML/CSS, and ASCII regression tables with multiple models side-by-side, plus summary …

gt

Build display tables from tabular data using a cohesive grammar of table parts (header, stub, body, footer). Enables …

R

modelsummary

Creates publication-quality tables summarizing multiple statistical models side-by-side, plus coefficient plots, data …

R

collapse

High-performance data transformation package designed by an economist. Provides fast grouped operations, time series …

R

tidyverse

Meta-package installing core tidyverse packages: ggplot2 (visualization), dplyr (manipulation), tidyr (tidying), readr …

R

Polars

Blazingly fast DataFrame library for Rust and Python with SQL-like syntax, lazy evaluation, and excellent time series …

Rust

countrycode

R package for converting between country naming and coding conventions essential for merging defense datasets

R

data.table

Extension of data.frame providing fast aggregation of large data (100GB+), ordered joins, and memory-efficient …

R

haven

Import and export Stata, SPSS, and SAS data files preserving variable labels and value labels. Handles .dta, .sav, …

R

DeclareDesign

Ex ante experimental design declaration and diagnosis. Enables researchers to formally describe their research design, …

R

fabricatr

Simulates realistic social science data for power analysis and design testing. Creates hierarchical data structures with …

R

Superpower

Simulation-based power analysis for factorial ANOVA designs (up to 3 factors). Includes Shiny app for interactive power …

R

ADOpy

Bayesian Adaptive Design Optimization (ADO) for tuning experiments in real-time, with models for psychometric tasks.

Python

pyDOE2

Implements classical Design of Experiments: factorial (full/fractional), response surface (Box-Behnken, CCD), Latin …

Python

WebPower

Comprehensive collection of tools for basic and advanced statistical power analysis including correlation, t-test, …

R

Adaptive

Parallel active learning library for adaptive function sampling/evaluation, with live plotting for monitoring.

Python

DoEgen

Automates generation and optimization of designs, especially for mixed factor-level experiments; computes efficiency …

Python

simChef

DGP (Data Generating Process) framework for systematic simulation studies. Enables reproducible computational …

R

Machine learning-based power analysis using surrogate models. Efficient sample size planning for complex study designs.

Ax (Meta Adaptive Experimentation)

Meta's open-source platform for adaptive experimentation. Bayesian optimization, multi-objective optimization, and …

Python

ContextualBandits

Implements a wide range of contextual bandit algorithms (linear, tree-based, neural) and off-policy evaluation methods.

Python

BayesianBandits

Lightweight microframework for Bayesian bandits (Thompson Sampling) with support for contextual/restless/delayed …

Python

MABWiser

Production-ready, scikit-learn style library for contextual & stochastic bandits with parallelism and simulation tools.

Python

Open Bandit Pipeline (OBP)

Framework for **offline evaluation (OPE)** of bandit policies using logged data. Implements IPS, DR, DM estimators.

Python

Sequential testing with always-valid inference. Supports continuous monitoring of A/B tests.

PyXAB

Library for advanced bandit problems: X-armed bandits (continuous/structured action spaces) and online optimization.

Python

SMPyBandits

Comprehensive research framework for single/multi-player MAB algorithms (stochastic, adversarial, contextual).

Python

contextual

Multi-armed bandit algorithms including Thompson Sampling, UCB, and LinUCB. Directly applicable to adaptive A/B testing …

R

randomizr

Proper randomization procedures for experiments with known assignment probabilities. Implements simple, complete, block, …

R

Estimates Average Marginal Component Effects (AMCEs) for conjoint experiments following Hainmueller, Hopkins & Yamamoto …

cregg

Tidy interface for conjoint analysis with visualization. Provides functions for calculating and plotting marginal means …

R

tea-tasting

Calculate A/B test statistics directly within data warehouses (BigQuery, ClickHouse, Snowflake, Spark) via Ibis …

Python

CausalMotifs

Meta's library for estimating heterogeneous spillover effects in A/B tests. Handles network interference.

Python

savvi

Safe Anytime Valid Inference using e-processes and confidence sequences (Ramdas et al. 2023). Valid inference at any …

Python

Ambrosia

End-to-end A/B testing from MobileTeleSystems with PySpark support. Covers experiment design, multi-group splitting, …

Spark

actuar

Actuarial science functions for R including loss distributions, credibility theory, ruin theory, and simulation of …

R

Comprehensive toolkit for extreme value analysis with diagnostic plots, return level estimation, and non-stationary …

chainladder-python

Python library for actuarial reserving implementing chain-ladder, Bornhuetter-Ferguson, Cape Cod, and stochastic methods …

Python

Fairlearn

Microsoft toolkit for assessing and improving ML model fairness, critical for insurance pricing compliance and avoiding …

Python

lifelib

Open-source actuarial library with complete life insurance projection models including term, whole life, universal life, …

Python

ChainLadder

Comprehensive R package for claims reserving methods including Mack, Munich, and bootstrap chain-ladder with full …

R

eiapy

Python wrapper for the EIA Open Data API. Access generation, consumption, prices, and other energy data …

Python

eiapy

Python wrapper for the EIA Open Data API. Access generation, consumption, prices, and other energy data …

Python

gridstatus

Unified Python interface for U.S. electricity grid data from all major ISOs

Python

OpenDSS

EPRI's open-source distribution system simulator. Quasi-static time-series analysis, DER integration, and comprehensive …

COM/Python

OpenDSS

EPRI's open-source distribution system simulator. Quasi-static time-series analysis, DER integration, and comprehensive …

COM/Python

pandapower

Power system analysis for distribution networks. Newton-Raphson power flow, state estimation, short circuit …

Python

pandapower

Power system analysis for distribution networks. Newton-Raphson power flow, state estimation, short circuit …

Python

GenX

Capacity expansion model from MIT/Princeton in Julia. Highly configurable with unit commitment, long-duration storage, …

Julia

GenX

Capacity expansion model from MIT/Princeton in Julia. Highly configurable with unit commitment, long-duration storage, …

Julia

PowerModels.jl

Power network optimization in Julia. Supports AC/DC optimal power flow, transmission expansion, and custom formulations …

Julia

LightweightMMM

Bayesian Marketing Mix Modeling (see Marketing Mix Models section).

Python

PyMC Marketing

Collection of Bayesian marketing models built with PyMC, including MMM, CLV, and attribution.

Python

PyMC-Marketing

Bayesian Marketing Mix Modeling and Customer Lifetime Value with PyMC, including GPU acceleration

Python

PyMC-Marketing

Bayesian Marketing Mix Modeling and Customer Lifetime Value with PyMC, including GPU acceleration

Python

mmm_stan

Python/STAN implementation of Bayesian Marketing Mix Models.

Python

pylift

Wayfair's uplift modeling wrapping sklearn for speed with rigorous Qini curve evaluation.

Python

UpliftML

Booking.com's enterprise uplift modeling via PySpark and H2O. Six meta-learners plus Uplift Random Forest with …

Python

MaMiMo

Lightweight Python library focused specifically on Marketing Mix Modeling implementation.

Python

Robyn

Meta's AI/ML-powered Marketing Mix Modeling package with ridge regression and multi-objective optimization

R

BTYDplus

Extended BTYD models for R including MBG/NBD, Pareto/GGG, and hierarchical Bayesian variants. Handles regular purchasing …

R

OSMnx

Download, model, analyze, and visualize street networks and urban infrastructure from OpenStreetMap. Essential for …

Python

conflictcartographer

Python package for conflict event data visualization and geospatial analysis

Python

Python client for the Space-Track.org API to access satellite catalog and TLE data

(PySAL Core)

The broader PySAL ecosystem contains many tools for spatial data handling, weights, visualization, and analysis.

Python

PySAL (spreg)

The spatial regression `spreg` module of PySAL. Implements spatial lag, error, IV models, and diagnostics.

Python

Apache Sedona

Distributed spatial analytics engine (formerly GeoSpark) with spatial SQL, K-NN joins, and range queries for spatial …

Spark

Skyfield

Elegant astronomy library for computing satellite and celestial positions using JPL ephemeris data

Python

sgp4

Implementation of the SGP4/SDP4 satellite propagation algorithms for processing TLE orbital data

Python

spdep

The foundational R package for spatial weights matrix creation and spatial autocorrelation testing. Provides functions …

R

xlogit

GPU-accelerated estimation of mixed logit models using CuPy/NumPy. Orders of magnitude faster than traditional packages …

Python

gtfs-kit

Analyze General Transit Feed Specification (GTFS) data. Compute route statistics, service frequencies, and visualize …

Python

Comprehensive R package for advanced choice modeling including mixed logit, latent class, hybrid choice, and integrated …

mlogit

The standard R package for multinomial logit estimation. Clean formula interface, nested logit support, and integration …

R

R package for generalized multinomial logit models including G-MNL, LC-MNL, and MM-MNL for flexible preference …

Fast maximum simulated likelihood estimation of mixed logit models in R. Optimized for speed with large datasets.

tidytransit

Read and analyze GTFS transit feeds in the tidyverse style. Integrates with sf for spatial analysis and dplyr for data …

R

hockeyR

R package for NHL play-by-play data with built-in expected goals models and player tracking statistics

R

worldfootballR

R package for scraping FBref, Transfermarkt, and Understat soccer data including xG, player values, and match statistics

R

nfl-data-py

Python package for accessing nflverse NFL play-by-play data with built-in EPA and win probability models

Python

Lahman

R package providing the complete Lahman Baseball Database as native R data frames for seamless analysis

R

nflfastR

R package for NFL play-by-play data with built-in expected points (EPA) and win probability models from 1999-present

R

nba_api

Full NBA Stats API wrapper with 127+ endpoints for accessing shot charts, player tracking, play-by-play, and historical …

Python

statsbombpy

Official Python API client for StatsBomb open data with 360 freeze-frame support for detailed soccer event analysis

Python

mplsoccer

Python library for football/soccer pitch visualization with support for heat maps, shot maps, pass maps, and event …

Python

pybaseball

Python library for pulling baseball data from Statcast, FanGraphs, Baseball Reference, and the Lahman database with …

Python

hoopR

R package for accessing NBA Stats API plus ESPN and KenPom data for comprehensive basketball analytics

R

BCEA

Bayesian Cost-Effectiveness Analysis in R. Processes MCMC output from JAGS/Stan, generates CEACs, CEAFs, and expected …

R

fhirclient

Official SMART on FHIR Python client. OAuth 2.0 authentication, resource CRUD operations, and search. Essential for …

Python

MONAI

Medical Open Network for AI - PyTorch-based framework for deep learning in healthcare imaging. Domain-specific …

Python

hesim

R package for health economic simulation modeling. Cohort discrete-time state transition models, partitioned survival …

R

heemod

Markov models for cost-effectiveness analysis in R. Define states, transitions, and costs/utilities with intuitive …

R

anthropic

Official Python SDK for Claude and Anthropic's API. Build AI applications with Claude models.

Python

openai

Official Python SDK for OpenAI's API. Access GPT-4, o1, DALL-E, embeddings, and other OpenAI models.

Python

crewai

Framework for orchestrating role-playing autonomous AI agents. Multi-agent collaboration made intuitive.

Python

openai-agents

OpenAI's lightweight, production-ready SDK for building agentic AI applications. Fast prototyping.

Python

langchain

Framework for developing LLM-powered applications. Chains, tools, memory, and retrieval.

Python

spaCy

Industrial-strength NLP library for efficient text processing pipelines (NER, POS tagging, etc.).

Python

langgraph

Framework for building stateful, multi-actor LLM applications. Graph-based agent workflows with persistence.

Python

EDSL

Expected Parrot Domain-Specific Language for designing and running LLM-powered surveys and experiments. Create AI agent …

Python

stm

Structural Topic Models incorporating document-level metadata as covariates affecting topic prevalence and content. …

R

tidytext

Tidy data principles for text mining. Converts text to tidy format (one-token-per-row), enabling analysis with dplyr, …

R

MLForecast

Scalable time series forecasting using machine learning models (e.g., LightGBM, XGBoost) as regressors.

Python

xgboost

Extreme Gradient Boosting implementing state-of-the-art gradient boosted decision trees. Highly efficient, scalable, and …

R

XGBoost

High-performance, optimized gradient boosting library (also supports RF). Known for speed, efficiency, and winning …

Python

xgboost

Extreme Gradient Boosting implementing state-of-the-art gradient boosted decision trees. Highly efficient, scalable, and …

R

NGBoost

Extends gradient boosting to probabilistic prediction, providing uncertainty estimates alongside point predictions. …

Python

ranger

Fast implementation of random forests particularly suited for high-dimensional data. Provides survival forests, …

R

LightGBM

Fast, distributed gradient boosting (also supports RF). Known for speed, low memory usage, and handling large datasets.

Python

LightGBM

Fast, distributed gradient boosting (also supports RF). Known for speed, low memory usage, and handling large datasets.

Python

CatBoost

Gradient boosting library excelling with categorical features (minimal preprocessing needed). Robust against …

Python

Efficient procedures for fitting regularized generalized linear models via penalized maximum likelihood. Implements …

DeepCTR

Easy-to-use implementations of deep CTR models including Wide&Deep, DeepFM, DIN, xDeepFM, and multi-task architectures

Python

RecBole

Comprehensive recommendation library with 100+ algorithms spanning general, sequential, context-aware, and …

Python

recommenderlab

R infrastructure for developing and evaluating recommender systems. Provides UBCF, IBCF, SVD, popular/random baselines …

R

Surprise

A Python scikit for building and analyzing recommender systems with explicit ratings. Implements SVD, SVD++, NMF, k-NN, …

Python

Surprise

A Python scikit for building and analyzing recommender systems with explicit ratings. Implements SVD, SVD++, NMF, k-NN, …

Python

LightFM

Hybrid recommendation library that handles cold-start by incorporating content features. Uses factorization machines to …

Python

Implicit

GPU-accelerated library for collaborative filtering on implicit feedback data. Implements ALS, BPR, and logistic matrix …

Python

DCA (Deep Count Autoencoder)

Denoising autoencoder for single-cell RNA-seq with ZINB output layer. Handles extreme sparsity in gene expression data.

Python

TensorFlow

Google's end-to-end open-source machine learning platform. Build and deploy ML models at scale.

Python

JAX

High-performance numerical computing with autograd and XLA compilation on CPU/GPU/TPU.

Python

scVI-tools

Single-cell Variational Inference. Deep generative models for single-cell RNA-seq with ZINB likelihood handling 80-95% …

Python

PyTorch

Popular deep learning framework with flexible automatic differentiation.

Python

VaDE

Variational Deep Embedding. VAE with Gaussian Mixture Model prior in latent space for deep clustering.

Python

SimPy

Process-based discrete-event simulation framework using Python generators. The standard for DES in Python with MIT …

Python

Stable-Baselines3

Reliable PyTorch implementations of A2C, DDPG, DQN, PPO, SAC, TD3 RL algorithms. Published in JMLR 2021.

Python

SuperSuit

Wrapper library for PettingZoo preprocessing including frame stacking, normalization, and action masking.

Python

NetLogoR

Pure R implementation of NetLogo framework—no NetLogo installation required. Benefits from ggplot2 integration and R …

R

TorchRL

Official PyTorch reinforcement learning library with TensorDict abstraction for modular RL development.

Python

Mesa

Leading open-source Python framework for agent-based modeling with spatial grids, agent schedulers, and Solara …

Python

simmer

Process-oriented discrete-event simulation for R with C++ core via Rcpp. Supports magrittr pipe workflow for building …

R

Industry-leading discrete-event simulation software from Rockwell Automation. Used by majority of Fortune 100 companies …

Embeds NetLogo into R for statistical analysis integration. Enables running NetLogo models and analyzing results in R …

Multi-method simulation platform supporting discrete-event, agent-based, and system dynamics modeling. Free Personal …

CleanRL

Single-file RL algorithm implementations (~340 lines each) for educational purposes and research. Published in JMLR …

Python

nlrx

rOpenSci package for NetLogo simulation via XML with BehaviorSpace support. Enables systematic NetLogo experiments from …

R

ABIDES

JPMorgan's agent-based interactive discrete event simulation for market microstructure research. NASDAQ-like exchange …

Python

OR-Gym

Operations research environments for RL including knapsack, bin packing, supply chain, newsvendor, and portfolio …

Python

Object-oriented discrete-event simulation with Process Digital Twin capabilities. Academic program offers free licenses …

ABCE

Agent-Based Computational Economics library from Oxford INET. Automatically handles trade with physically consistent …

Python

pyNetLogo

Python-NetLogo interface enabling SALib sensitivity analysis integration and parallel NetLogo simulations. Published in …

Python

OpenSpiel

DeepMind's 70+ game environments with multi-agent RL algorithms including Alpha-Rank, Neural Fictitious Self-Play, and …

Python

pygambit

N-player extensive form games with Alan Turing Institute support. Computes Nash, perfect, and sequential equilibria.

Python

Nashpy

Computation of Nash equilibria for 2-player games. Support enumeration and Lemke-Howson algorithm.

Python

fairpy

Fair division algorithms from academic papers. Implements cake-cutting and item allocation procedures.

Python

AuctionGym

Amazon's ad auction simulator for first/second-price auctions with RL bidding agents. Best Paper at AdKDD 2022.

Python

deep-opt-auctions

Neural network optimal auction design. Implements RegretNet, RochetNet for mechanism design.

Python

fairpyx

Course-seat allocation with capacity constraints. Practical fair division for university course assignment.

Python

scarfmatch

Matching with couples using Scarf's algorithm. Essential for NRMP-style medical residency matching.

Python

PettingZoo

Multi-agent version of Gymnasium with Agent-Environment-Cycle (AEC) model. Includes card games, MPE, and cooperative …

Python

AI Economist

Salesforce's two-level RL environment for tax policy design. Published in Science Advances 2022. Includes COVID-19 …

Python

HiGHS

State-of-the-art open-source LP/MIP solver. Now the default solver in PyPSA, JuMP, and SciPy. Competitive with …

C++/Python

HiGHS

State-of-the-art open-source LP/MIP solver. Now the default solver in PyPSA, JuMP, and SciPy. Competitive with …

C++/Python

Pyomo

General-purpose algebraic optimization modeling in Python. Supports LP, MILP, NLP, and stochastic programming with …

Python

Pyomo

General-purpose algebraic optimization modeling in Python. Supports LP, MILP, NLP, and stochastic programming with …

Python

ortools

Google's operations research toolkit. Constraint programming, routing, linear/integer programming, and scheduling.

Python

Python interface for Gurobi, the best-in-class commercial solver. LP, QP, MIP, and MIQP.

Faer

State-of-the-art linear algebra for Rust with Cholesky, QR, SVD decompositions and multithreaded solvers for large …

Rust

scipy.optimize

Optimization algorithms built into SciPy. Minimization, root finding, curve fitting, and linear programming.

Python

Collection of tests for diagnostic checking in linear regression models. Provides the essential coeftest() function for …

Statsmodels

Comprehensive library for estimating statistical models (OLS, GLM, etc.), conducting tests, and data exploration. Core …

Python

Functions accompanying 'An R Companion to Applied Regression.' Provides advanced regression diagnostics including …

lmerTest

Provides p-values for lme4 model fits via Satterthwaite's or Kenward-Roger degrees of freedom methods, with Type …

R

Linregress

Simple linear regression for Rust with R-style formula syntax, standard errors, t-stats, and p-values.

Rust

expectation

E-values and game-theoretic probability for sequential testing. Enables early signal detection with proper error …

Python

broom

Converts messy output from 100+ statistical model types into consistent tidy tibbles using three verbs: tidy() for …

R

gtsummary

Creates publication-ready analytical and summary tables (Table 1 demographics, regression results, survival analyses) …

R

hypothetical

Library focused on hypothesis testing: ANOVA/MANOVA, t-tests, chi-square, Fisher's exact, nonparametric tests …

Python

PyWhy-Stats

Part of the PyWhy ecosystem providing statistical methods specifically for causal applications, including various …

Python

maketables

Publication-ready regression tables for pyfixest, statsmodels, linearmodels. Outputs HTML (great-tables), LaTeX, Word.

Python

HypoRS

Hypothesis testing library for Rust with T-tests, Z-tests, ANOVA, Chi-square, designed to work seamlessly with Polars …

Rust

Statrs

Comprehensive statistical distributions for Rust (Normal, T, Gamma, etc.) with PDF, CDF, quantile functions—the …

Rust

Pingouin

User-friendly interface for common statistical tests (ANOVA, ANCOVA, t-tests, correlations, chi², reliability) built on …

Python

Stargazer

Python port of R's stargazer for creating publication-quality regression tables (HTML, LaTeX) from `statsmodels` & …

Python

marginaleffects

Modern standard for interpreting regression results—up to 1000× faster than margins. Computes marginal effects, …

R

texreg

Converts coefficients, standard errors, significance stars, and fit statistics from statistical models into LaTeX, HTML, …

R

Python Packages for Applied Economists

Curated collection of Python packages for applied researchers organized by functionality.

Python

Classic bootstrap methods implementing the approaches described in Davison & Hinkley (1997). Provides functions for both …

fwildclusterboot

Fast wild cluster bootstrap implementation following Roodman et al. (2019)—up to 1000× faster than alternatives. …

R

rsample

Modern tidyverse-compatible resampling infrastructure. Provides functions for creating resamples (bootstrap, …

R

SciPy Bootstrap

(`scipy.stats.bootstrap`) Computes bootstrap confidence intervals for various statistics using percentile, BCa methods.

Python

recombinator

Block bootstrap methods including Moving Block, Circular Block, Stationary, and Tapered Block Bootstrap for time series.

Python

Bootstrap unit root tests with sieve and wild bootstrap methods for time series stationarity testing.

wildboottest

Fast implementation of various wild cluster bootstrap algorithms (WCR, WCU) for robust inference, especially with few …

Python

Wild cluster bootstrap and pairs cluster bootstrap implementations for clustered standard errors.

Bambi

High-level interface for building Bayesian GLMMs, built on top of PyMC. Uses formula syntax similar to R's `lme4`.

Python

Bayesian Structural Time Series providing the foundation for CausalImpact. Supports spike-and-slab variable selection, …

NumPyro

Probabilistic programming library built on JAX for scalable Bayesian inference, often faster than PyMC.

Python

PyMC

Flexible probabilistic programming library for Bayesian modeling and inference using MCMC algorithms (NUTS).

Python

PyMC

Flexible probabilistic programming library for Bayesian modeling and inference using MCMC algorithms (NUTS).

Python

rstanarm

Pre-compiled Bayesian regression models using Stan that mimic familiar R functions (lm, glm, lmer) with customary …

R

brms

High-level interface for fitting Bayesian generalized multilevel models using Stan, with lme4-style formula syntax …

R

bayesplot

Extensive library of ggplot2-based plotting functions for posterior analysis, MCMC diagnostics, and prior/posterior …

R

rstan

Core R interface to the Stan probabilistic programming language, providing full Bayesian inference via NUTS/HMC, …

R

Pyro

Deep universal probabilistic programming on PyTorch. Special support for Bayesian neural networks, normalizing flows, …

Python

mstate

Multi-state models in R. Handles competing risks, illness-death models, and complex disease progressions. Estimation, …

R

survminer

Visualization tools for survival analysis in R with publication-ready Kaplan-Meier plots, risk tables, and Cox model …

R

survHE

Survival analysis for health economics in R. Fits multiple parametric distributions, extrapolates survival curves, and …

R

scikit-survival

Machine learning for survival analysis compatible with scikit-learn, including gradient boosted models, random survival …

Python

scikit-survival

Machine learning for survival analysis compatible with scikit-learn, including gradient boosted models, random survival …

Python

survival

Core R package for survival analysis with Cox regression, Kaplan-Meier estimation, and parametric survival models - the …

R

survival

Core R package for survival analysis with Cox regression, Kaplan-Meier estimation, and parametric survival models - the …

R

flexsurv

Flexible parametric survival models including spline-based hazards, multi-state models, and cure models for complex …

R

pycox

PyTorch-based survival analysis. Implements DeepSurv, DeepHit, Cox-Time, and other neural survival models with partial …

Python

lifelines

Comprehensive library for survival analysis: Kaplan-Meier, Nelson-Aalen, Cox regression, AFT models, handling censored …

Python

DataSynthesizer

Privacy-preserving synthetic data using Bayesian networks with differential privacy. From University of Washington …

Python

Synthpop

Port of the R package for generating synthetic populations based on sample survey data.

Python

CTGAN

GAN-based tabular data synthesizer using Variational GMM for mode-specific normalization. Published at NeurIPS 2019. …

Python

Faker

Comprehensive fake data generator for 50+ locales including names, addresses, financial data, and more. Most popular …

Python

DeepEcho

Time series synthetic data generation using deep learning. Part of the SDV ecosystem for sequential data.

Python

Gretel Synthetics

Open-source synthetic data library with DGAN for time series, ACTGAN, and differential privacy support from Gretel.ai.

Python

Mimesis

High-performance fake data generator—faster than Faker. Provides data for multiple domains and 35+ locales.

Python

Synthetic population simulation for EU-SILC style survey data. Creates realistic household and individual-level …

SDV (Synthetic Data Vault)

Comprehensive library for generating synthetic tabular, relational, and time series data using various models.

Python

sdcMicro

Statistical Disclosure Control for microdata used by World Bank and census agencies. Comprehensive anonymization …

R

NetworkCausalTree

Estimates both direct treatment effects and spillover effects under clustered network interference (Bargagli-Stoffi et …

Python

ggraph

Grammar of graphics for network data built on ggplot2. Provides layouts, geometries, and faceting specifically designed …

R

igraph

Comprehensive network analysis library with efficient algorithms for network creation, manipulation, and analysis. …

R

igraph

Comprehensive network analysis library with efficient algorithms for network creation, manipulation, and analysis. …

R

tidygraph

Tidy data interface for network/graph data. Extends dplyr verbs to work with nodes and edges, enabling pipe-friendly …

R

Social network analysis tools including network visualization, centrality measures, and statistical models for network …

Free tool for discovering academic papers through network visualization of paper connections and co-authorships.

python-louvain

Community detection in large networks using the Louvain algorithm, applicable to defense network analysis

Python

fortuna

AWS library for uncertainty quantification in deep learning. Bayesian and conformal methods.

Python

TorchCP

PyTorch-native conformal prediction for DNNs, GNNs, and LLMs with GPU acceleration.

Python

puncc

IRT Lab's library for predictive uncertainty with conformal prediction. Supports various conformal methods.

Python

MAPIE

Scikit-learn-contrib library for conformal prediction intervals. Provides model-agnostic uncertainty quantification for …

Python

crepes

Lightweight library for conformal regressors and predictive systems. Simple API for calibrated prediction intervals.

Python

urca

Implements unit root and cointegration tests commonly used in applied econometric analysis. Includes Augmented …

R

pmdarima

ARIMA modeling with automatic parameter selection (auto-ARIMA), similar to R's `forecast::auto.arima`.

Python

ARCH

Specialized library for modeling and forecasting conditional volatility using ARCH, GARCH, EGARCH, and related models.

Python

Kats

Broad toolkit for time series analysis, including multivariate analysis, detection (outliers, change points, trends), …

Python

vars

Comprehensive package for Vector Autoregression (VAR), Structural VAR (SVAR), and Structural Vector Error Correction …

R

KFAS

State space modeling framework for exponential family time series with computationally efficient Kalman filtering, …

R

Maximum likelihood and Bayesian analysis of Normal linear state space models (Dynamic Linear Models). Features …

forecast

The foundational R package for univariate time series forecasting. Provides methods for exponential smoothing via state …

R

Metran

Specialized package for estimating Dynamic Factor Models (DFM) using state-space methods and Kalman filtering.

Python

StatsForecast

Fast, scalable implementations of popular statistical forecasting models (ETS, ARIMA, Theta, etc.) optimized for …

Python

LocalProjections

Community implementations of Jordà (2005) Local Projections for estimating impulse responses without VAR assumptions.

Python

fable

A tidyverse-native forecasting framework providing ETS, ARIMA, and other models for tidy time series (tsibble objects). …

R

Augurs

Time series forecasting and analysis for Rust with ETS, MSTL decomposition, seasonality detection, outlier detection, …

Rust

Testing, monitoring, and dating structural changes in linear regression models. Implements the generalized fluctuation …

TS-Flint

Two Sigma's time-series library for Spark with optimized temporal joins, as-of joins, and distributed OLS for …

Spark

Prophet

Forecasting procedure for time series with strong seasonality and trend components, developed by Facebook.

Python

prophet

Automatic forecasting procedure based on an additive decomposable model with non-linear trends, yearly/weekly/daily …

R

NeuralForecast

Deep learning models (N-BEATS, N-HiTS, Transformers, RNNs) for time series forecasting, built on PyTorch Lightning.

Python

PyKalman

Implements Kalman filter, smoother, and EM algorithm for parameter estimation, including support for missing values and …

Python