├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Pranjal Rawat 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Econometrics in Python 2 | 3 | [![Python Version](https://img.shields.io/badge/python-3.7%2B-blue)](https://www.python.org/downloads/) 4 | [![License](https://img.shields.io/badge/license-MIT-green)](LICENSE) 5 | [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen)](CONTRIBUTING.md) 6 | 7 | A comprehensive collection of Python packages for econometrics, causal inference, quantitative economics, and data analysis. This repository serves as a reference guide for researchers, data scientists, economists, and practitioners working with economic data. Most packages can be installed via `pip`. 8 | 9 | ## Contents 10 | 11 | - [Core Libraries & Linear Models](#core-libraries--linear-models) 12 | - [Statistical Inference & Hypothesis Testing](#statistical-inference--hypothesis-testing) 13 | - [Power Simulation & Design of Experiments](#power-simulation--design-of-experiments) 14 | - [Panel Data & Fixed Effects](#panel-data--fixed-effects) 15 | - [Instrumental Variables (IV) & GMM](#instrumental-variables-iv--gmm) 16 | - [Causal Inference & Matching](#causal-inference--matching) 17 | - [Causal Discovery & Graphical Models](#causal-discovery--graphical-models) 18 | - [Double/Debiased Machine Learning (DML)](#doubledebiased-machine-learning-dml) 19 | - [Program Evaluation Methods (DiD, SC, RDD)](#program-evaluation-methods-did-sc-rdd) 20 | - [Adaptive Experimentation & Bandits](#adaptive-experimentation--bandits) 21 | - [Tree & Ensemble Methods for Prediction](#tree--ensemble-methods-for-prediction) 22 | - [Time Series Forecasting](#time-series-forecasting) 23 | - [Time Series Econometrics](#time-series-econometrics) 24 | - [State Space & Volatility Models](#state-space--volatility-models) 25 | - [Discrete Choice Models](#discrete-choice-models) 26 | - [Structural Econometrics & Estimation](#structural-econometrics--estimation) 27 | - [Quantile Regression & Distributional Methods](#quantile-regression--distributional-methods) 28 | - [Bayesian Econometrics](#bayesian-econometrics) 29 | - [Marketing Mix Models (MMM) & Business Analytics](#marketing-mix-models-mmm--business-analytics) 30 | - [Spatial Econometrics](#spatial-econometrics) 31 | - [Dimensionality Reduction](#dimensionality-reduction) 32 | - [Natural Language Processing Tools](#natural-language-processing-tools) 33 | - [Synthetic Data Generation](#synthetic-data-generation) 34 | - [Numerical Optimization & Computational Tools](#numerical-optimization--computational-tools) 35 | - [Standard Errors, Bootstrapping & Reporting](#standard-errors-bootstrapping--reporting) 36 | - [Learning Resources](#learning-resources) 37 | - [Contributing](#contributing) 38 | 39 | --- 40 | 41 | ## Core Libraries & Linear Models 42 | 43 | Fundamental packages for statistical modeling, regression, and core econometric tasks. 44 | 45 | | Package | Description | Links | Installation | 46 | |-----------------|------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------|--------------------------| 47 | | **Statsmodels** | Comprehensive library for estimating statistical models (OLS, GLM, etc.), conducting tests, and data exploration. Core tool. | [Docs](https://www.statsmodels.org/) • [GitHub](https://github.com/statsmodels/statsmodels) | `pip install statsmodels` | 48 | | **Scikit-learn**| Foundational ML library with regression models (incl. regularized), model selection, cross-validation, evaluation metrics. | [Docs](https://scikit-learn.org/) • [GitHub](https://github.com/scikit-learn/scikit-learn) | `pip install scikit-learn` | 49 | 50 | --- 51 | 52 | ## Statistical Inference & Hypothesis Testing 53 | 54 | Packages providing functions for classical hypothesis testing, group comparisons, survival/duration analysis, and related statistical inference. 55 | 56 | | Package | Description | Links | Installation | 57 | |-----------------|---------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|--------------------------| 58 | | **Scipy.stats** | Foundational module within SciPy for a wide range of statistical functions, distributions, and hypothesis tests (t-tests, ANOVA, chi², KS, etc.). | [Docs](https://docs.scipy.org/doc/scipy/reference/stats.html) • [GitHub](https://github.com/scipy/scipy) | `pip install scipy` | 59 | | **Statsmodels** | Includes dedicated modules for statistical tests (`stats`), ANOVA (`anova`), nonparametric methods, multiple testing corrections, contingency tables. | [Docs (stats)](https://www.statsmodels.org/stable/stats.html) • [GitHub](https://github.com/statsmodels/statsmodels) | `pip install statsmodels` | 60 | | **Pingouin** | User-friendly interface for common statistical tests (ANOVA, ANCOVA, t-tests, correlations, chi², reliability) built on pandas & scipy. | [Docs](https://pingouin-stats.org/) • [GitHub](https://github.com/raphaelvallat/pingouin) | `pip install pingouin` | 61 | | **hypothetical**| Library focused on hypothesis testing: ANOVA/MANOVA, t-tests, chi-square, Fisher's exact, nonparametric tests (Mann-Whitney, Kruskal-Wallis, etc.). | [GitHub](https://github.com/aschleg/hypothetical) | `pip install hypothetical` | 62 | | **lifelines** | Comprehensive library for survival analysis: Kaplan-Meier, Nelson-Aalen, Cox regression, AFT models, handling censored data. | [Docs](https://lifelines.readthedocs.io/en/latest/) • [GitHub](https://github.com/CamDavidsonPilon/lifelines) | `pip install lifelines` | 63 | | **PyWhy-Stats** | Part of the PyWhy ecosystem providing statistical methods specifically for causal applications, including various independence tests and power-divergence methods. | [Docs](https://pywhy-stats.readthedocs.io/) • [GitHub](https://github.com/py-why/pywhy-stats) | `pip install pywhy-stats` | 64 | 65 | --- 66 | 67 | ## Power Simulation & Design of Experiments 68 | 69 | Tools for calculating statistical power, determining sample sizes, generating experimental designs (DoE), and implementing adaptive experimentation methods. 70 | 71 | | Package | Description (Focus) | Links | Installation | 72 | |---------------------|------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------|------------------------| 73 | | **Statsmodels** | Includes `stats.power` module for power/sample size calculations for t-tests, F-tests, Z-tests, Chi-squared tests. | [Docs (Power)](https://www.statsmodels.org/stable/stats.html#power-and-sample-size-calculations) • [GitHub](https://github.com/statsmodels/statsmodels) | `pip install statsmodels`| 74 | | **pyDOE2** | Implements classical Design of Experiments: factorial (full/fractional), response surface (Box-Behnken, CCD), Latin Hypercube. | [Docs](https://pythonhosted.org/pyDOE2/) • [GitHub](https://github.com/clicumu/pyDOE2) | `pip install pyDOE2` | 75 | | **DoEgen** | Automates generation and optimization of designs, especially for mixed factor-level experiments; computes efficiency metrics. | [GitHub](https://github.com/sebhaan/DoEgen) | `pip install DoEgen` | 76 | | **ADOpy** | Bayesian Adaptive Design Optimization (ADO) for tuning experiments in real-time, with models for psychometric tasks. | [Docs](https://adopy.readthedocs.io/en/latest/) • [GitHub](https://github.com/adopy/adopy) | `pip install adopy` | 77 | | **Adaptive** | Parallel active learning library for adaptive function sampling/evaluation, with live plotting for monitoring. | [Docs](https://adaptive.readthedocs.io/en/latest/) • [GitHub](https://github.com/python-adaptive/adaptive) | `pip install adaptive` | 78 | 79 | --- 80 | 81 | ## Panel Data & Fixed Effects 82 | 83 | Tools for modeling data with both cross-sectional and time dimensions, including high-dimensional fixed effects. 84 | 85 | | Package | Description | Links | Installation | 86 | |--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------|--------------------------------------| 87 | | **Linearmodels** | Estimation of fixed, random, pooled OLS models for panel data. Also Fama-MacBeth and between/first-difference estimators. | [Docs](https://bashtage.github.io/linearmodels/) • [GitHub](https://github.com/bashtage/linearmodels) | `pip install linearmodels` | 88 | | **PyFixest** | Fast estimation of linear models with multiple high-dimensional fixed effects (like R's `fixest`). Supports OLS, IV, Poisson, robust/cluster SEs. | [Docs & GitHub](https://github.com/py-econometrics/pyfixest) | `pip install pyfixest` | 89 | | **pydynpd** | Estimation of dynamic panel data models using Arellano-Bond (Difference GMM) and Blundell-Bond (System GMM). Includes Windmeijer correction & tests. | [Docs (JOSS)](https://doi.org/10.21105/joss.04416) • [GitHub](https://github.com/dazhwu/pydynpd) | `pip install pydynpd` | 90 | | **FixedEffectModelPyHDFE** | Solves linear models with high-dimensional fixed effects, supporting robust variance calculation and IV. | [PyPI](https://pypi.org/project/FixedEffectModelPyHDFE/) | `pip install FixedEffectModelPyHDFE` | 91 | | **duckreg** | Out-of-core regression (OLS/IV) for very large datasets using DuckDB aggregation. Handles data that doesn't fit in memory. | [Docs & GitHub](https://github.com/py-econometrics/duckreg) | `pip install duckreg` | 92 | 93 | --- 94 | 95 | ## Instrumental Variables (IV) & GMM 96 | 97 | Packages for handling endogeneity using instrumental variables and generalized method of moments. 98 | 99 | | Package | Description | Links | Installation | 100 | |---------------------------|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|--------------------------| 101 | | **Linearmodels** | Implements single-equation IV estimators (2SLS, LIML, GMM-IV) and system GMM (SUR, 3SLS). | [Docs](https://bashtage.github.io/linearmodels/) • [GitHub](https://github.com/bashtage/linearmodels) | `pip install linearmodels` | 102 | | **py-econometrics `gmm`** | Lightweight package for setting up and estimating custom GMM models based on user-defined moment conditions. | [Docs & GitHub](https://github.com/py-econometrics/gmm) | `pip install gmm` | 103 | | **Statsmodels** | Includes basic IV/2SLS and GMM implementations within its broader framework. | [Docs](https://www.statsmodels.org/) • [GitHub](https://github.com/statsmodels/statsmodels) | `pip install statsmodels` | 104 | 105 | --- 106 | 107 | ## Causal Inference & Matching 108 | 109 | Tools for estimating causal effects using observational data, focusing on matching and structural approaches. 110 | 111 | | Package | Description | Links | Installation | 112 | |--------------------|--------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------|---------------------------| 113 | | **DoWhy** | End-to-end framework for causal inference based on causal graphs (DAGs) and potential outcomes. Covers identification, estimation, refutation. | [Docs](https://www.pywhy.org/dowhy/) • [GitHub](https://github.com/py-why/dowhy) | `pip install dowhy` | 114 | | **CausalInference**| Implements classical causal inference methods like propensity score matching, inverse probability weighting, stratification. | [Docs](https://causalinferenceinpython.org) • [GitHub](https://github.com/laurencium/causalinference) | `pip install CausalInference` | 115 | | **CausalML** | Focuses on uplift modeling and heterogeneous treatment effect estimation using machine learning techniques. | [Docs](https://causalml.readthedocs.io/) • [GitHub](https://github.com/uber/causalml) | `pip install causalml` | 116 | | **CausalMatch** | Implements Propensity Score Matching (PSM) and Coarsened Exact Matching (CEM) with ML flexibility for propensity score estimation. | [Docs & GitHub](https://github.com/bytedance/CausalMatch) | `pip install causalmatch` | 117 | | **fastmatch** | Fast k-nearest-neighbor matching for large datasets using Facebook's FAISS library. | [Docs & GitHub](https://github.com/py-econometrics/fastmatch) | `pip install fastmatch` | 118 | | **scikit-uplift**| Focuses on uplift modeling and estimating heterogeneous treatment effects using various ML-based methods. | [Docs](https://scikit-uplift.readthedocs.io/en/latest/) • [GitHub](https://github.com/maks-sh/scikit-uplift) | `pip install scikit-uplift`| 119 | | **CausalPy** | Developed by PyMC Labs, focuses specifically on causal inference in quasi-experimental settings. Specializes in scenarios where randomization is impossible or expensive. | [Docs](https://www.pymc-marketing.io/) • [GitHub](https://github.com/pymc-labs/pymc-marketing) | `pip install CausalPy` | 120 | | **CausalLib** | IBM-developed package that provides a scikit-learn-inspired API for causal inference with meta-algorithms supporting arbitrary machine learning models. | [Docs](https://causallib.readthedocs.io/) • [GitHub](https://github.com/IBM/causallib) | `pip install causallib` | 121 | | **CausalPlayground** | Python library for causal research that addresses the scarcity of real-world datasets with known causal relations. Provides fine-grained control over structural causal models. | [Docs](https://causal-playground.readthedocs.io/) • [GitHub](https://github.com/causal-playground/causal-playground) | `pip install causal-playground` | 122 | 123 | --- 124 | 125 | ## Causal Discovery & Graphical Models 126 | 127 | Libraries focused on learning causal structures (DAGs, Bayesian Networks) from data and performing inference using graphical models. 128 | 129 | | Package | Description (Focus) | Links | Installation | 130 | |-------------------------------------|---------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------|---------------------------| 131 | | **Ananke** | Causal inference using graphical models (DAGs), including identification theory and effect estimation. | [Docs](https://ananke.readthedocs.io/) • [GitHub]([https://github.com/py-why/Ananke](https://github.com/ghosthamlet/ananke)) | `pip install ananke-causal` | 132 | | **CausalNex** | Uses Bayesian Networks for causal reasoning, combining ML with expert knowledge to model relationships. | [GitHub](https://github.com/microsoft/causalnex) | `pip install causalnex` | 133 | | **Causal Discovery Toolbox (CDT)** | Implements algorithms for causal discovery (recovering causal graph structure) from observational data. | [Docs](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/index.html) • [GitHub](https://github.com/FenTechSolutions/CausalDiscoveryToolbox) | `pip install cdt` | 134 | | **DoWhy** | (See Causal Inference) Includes functionality for modeling assumptions with causal graphs (DAGs). | [Docs](https://www.pywhy.org/dowhy/) • [GitHub](https://github.com/py-why/dowhy) | `pip install dowhy` | 135 | | **Tigramite** | Specialized package for causal inference in time series data implementing PCMCI, PCMCIplus, LPCMCI algorithms with conditional independence tests. | [Docs](https://tigramite.readthedocs.io/) • [GitHub](https://github.com/jakobrunge/tigramite) | `pip install tigramite` | 136 | | **gCastle** | Huawei Noah's Ark Lab end-to-end causal structure learning toolchain emphasizing gradient-based methods with GPU acceleration (NOTEARS, GOLEM). | [Docs](https://gcastle.readthedocs.io/) • [GitHub](https://github.com/huawei-noah/trustworthyAI) | `pip install gcastle` | 137 | | **causal-learn** | Comprehensive Python package serving as Python translation and extension of Java-based Tetrad toolkit for causal discovery algorithms. | [Docs](https://causal-learn.readthedocs.io/) • [GitHub](https://github.com/py-why/causal-learn) | `pip install causal-learn` | 138 | | **LiNGAM** | Specialized package for learning non-Gaussian linear causal models, implementing various versions of the LiNGAM algorithm including ICA-based methods. | [Docs](https://lingam.readthedocs.io/) • [GitHub](https://github.com/cdt15/lingam) | `pip install lingam` | 139 | | **py-tetrad** | Python interface to Tetrad Java library using JPype, providing direct access to Tetrad's causal discovery algorithms with efficient data translation. | [GitHub](https://github.com/py-why/py-tetrad) | Available on GitHub (installation via git clone) | 140 | 141 | --- 142 | 143 | ## Double/Debiased Machine Learning (DML) 144 | 145 | Methods combining machine learning and econometrics for robust causal inference in high-dimensional settings. 146 | 147 | | Package | Description | Links | Installation | 148 | |--------------|--------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|---------------------| 149 | | **DoubleML** | Implements the double/debiased ML framework (Chernozhukov et al.) for estimating causal parameters (ATE, LATE, POM) with ML nuisances. | [Docs](https://docs.doubleml.org/) • [GitHub](https://github.com/DoubleML/doubleml-for-py) | `pip install DoubleML`| 150 | | **EconML** | Microsoft toolkit for estimating heterogeneous treatment effects using DML, causal forests, meta-learners, and orthogonal ML methods. | [Docs](https://econml.azurewebsites.net/) • [GitHub](https://github.com/py-why/EconML) | `pip install econml` | 151 | | **pydoublelasso** | Double‑post Lasso estimator for high‑dimensional treatment effects (Belloni‑Chernozhukov‑Hansen 2014). | [PyPI](https://pypi.org/project/pydoublelasso/) | `pip install pydoublelasso` | 152 | | **pyhtelasso** | Debiased‑Lasso detector of heterogeneous treatment effects in randomized experiments. | [PyPI](https://pypi.org/project/pyhtelasso/) | `pip install pyhtelasso` | 153 | 154 | --- 155 | 156 | ## Program Evaluation Methods (DiD, SC, RDD) 157 | 158 | Quasi-experimental methods for estimating causal effects from policy changes or natural experiments. 159 | 160 | | Package | Description | Links | Installation | 161 | |-----------------------------|----------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|-----------------------------------| 162 | | **csdid** | Python adaptation of the R `did` package. Implements multi-period DiD with staggered treatment timing (Callaway & Sant’Anna). | [GitHub](https://github.com/d2cml-ai/csdid) | `pip install csdid` | 163 | | **Differences** | Implements modern difference-in-differences methods for staggered adoption designs (e.g., Callaway & Sant'Anna). | [Docs](https://bernardodionisi.github.io/differences/) • [GitHub](https://github.com/bernardodionisi/differences) | `pip install differences` | 164 | | **SyntheticControlMethods** | Implementation of synthetic control methods for comparative case studies when panel data is available. | [GitHub](https://github.com/OscarEngelbrektson/SyntheticControlMethods) | `pip install SyntheticControlMethods` | 165 | | **mlsynth** | Implements advanced synthetic control methods: forward DiD, cluster SC, factor models, and proximal SC. Designed for single-treated-unit settings. | [Docs](https://mlsynth.readthedocs.io/en/latest/) • [GitHub](https://github.com/jaredjgreathouse/mlsynth) | `pip install mlsynth` | 166 | | **rdrobust** | Comprehensive tools for Regression Discontinuity Designs (RDD), including optimal bandwidth selection, estimation, inference. | [GitHub](https://github.com/rdpackages/rdrobust) • [PyPI](https://pypi.org/project/rdrobust/) | `pip install rdrobust` | 167 | | **rdd** | Toolkit for sharp RDD analysis, including bandwidth calculation and estimation, integrating with pandas. | [GitHub](https://github.com/evan-magnusson/rdd) | `pip install rdd` | 168 | | **CausalImpact** | Python port of Google's R package for estimating causal effects of interventions on time series using Bayesian structural time-series models. | [Docs](https://google.github.io/CausalImpact/CausalImpact/CausalImpact.html) (R) • [GitHub (Py)](https://github.com/tcassou/causal_impact) | `pip install causalimpact` | 169 | | **pycinc** | Changes‑in‑Changes (CiC) estimator for distributional treatment effects (Athey & Imbens 2006). | [PyPI](https://pypi.org/project/pycinc/) | `pip install pycinc` | 170 | | **pyleebounds** | Lee (2009) sample‑selection bounds for treatment effects; trims treated distribution to match selection rates. | [PyPI](https://pypi.org/project/pyleebounds/) | `pip install pyleebounds` | 171 | 172 | --- 173 | 174 | ## Adaptive Experimentation & Bandits 175 | 176 | Libraries for designing and evaluating adaptive experiments using multi-armed bandit (MAB) algorithms, covering stochastic, contextual, and more complex bandit settings. 177 | 178 | | Package | Description (Focus) | Links | Installation | 179 | |-----------------------------|-------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------|---------------------------| 180 | | **SMPyBandits** | Comprehensive research framework for single/multi-player MAB algorithms (stochastic, adversarial, contextual). | [Docs](https://smpybandits.github.io/) • [GitHub](https://github.com/SMPyBandits/SMPyBandits) | `pip install SMPyBandits` | 181 | | **MABWiser** | Production-ready, scikit-learn style library for contextual & stochastic bandits with parallelism and simulation tools. | [Docs](https://fidelity.github.io/mabwiser/) • [GitHub](https://github.com/fidelity/mabwiser) | `pip install mabwiser` | 182 | | **ContextualBandits** | Implements a wide range of contextual bandit algorithms (linear, tree-based, neural) and off-policy evaluation methods. | [Docs](https://contextual-bandits.readthedocs.io/) • [GitHub](https://github.com/david-cortes/contextualbandits) | `pip install contextualbandits` | 183 | | **BayesianBandits** | Lightweight microframework for Bayesian bandits (Thompson Sampling) with support for contextual/restless/delayed rewards. | [Docs](https://rukulkarni.com/projects/bayesianbandits/) • [GitHub](https://github.com/IntelyCare/bayesianbandits) | `pip install bayesianbandits` | 184 | | **Open Bandit Pipeline (OBP)**| Framework for **offline evaluation (OPE)** of bandit policies using logged data. Implements IPS, DR, DM estimators. | [Docs](https://zr-obp.readthedocs.io/en/latest/) • [GitHub](https://github.com/st-tech/zr-obp) | `pip install obp` | 185 | | **PyXAB** | Library for advanced bandit problems: X-armed bandits (continuous/structured action spaces) and online optimization. | [Docs](https://pyxab.readthedocs.io/en/latest/) • [GitHub](https://github.com/huanzhang12/pyxab) | `pip install pyxab` | 186 | 187 | --- 188 | 189 | ## Tree & Ensemble Methods for Prediction 190 | 191 | Powerful machine learning techniques like Random Forests and Gradient Boosting, often used for prediction, feature importance, and handling complex non-linear relationships. This section covers leading packages for both types of ensemble methods. 192 | 193 | | Package | Description | Links | Installation | 194 | |-----------------------|------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------| 195 | | **Scikit-learn Ens.** | (`RandomForestClassifier`/`Regressor`) Widely-used, versatile implementation of Random Forests. Easy API and parallel processing support. | [Docs](https://scikit-learn.org/stable/modules/ensemble.html#random-forests) • [GitHub](https://github.com/scikit-learn/scikit-learn) | `pip install scikit-learn` | 196 | | **cuML (RAPIDS)** | GPU-accelerated implementation of Random Forests for significant speedups on large datasets. Scikit-learn compatible API. | [Docs](https://docs.rapids.ai/api/cuml/stable/) • [GitHub](https://github.com/rapidsai/cuml) | `conda install ...` (See RAPIDS docs) | 197 | | **XGBoost** | High-performance, optimized gradient boosting library (also supports RF). Known for speed, efficiency, and winning competitions. | [Docs](https://xgboost.readthedocs.io/) • [GitHub](https://github.com/dmlc/xgboost) | `pip install xgboost` | 198 | | **LightGBM** | Fast, distributed gradient boosting (also supports RF). Known for speed, low memory usage, and handling large datasets. | [Docs](https://lightgbm.readthedocs.io/) • [GitHub](https://github.com/microsoft/LightGBM) | `pip install lightgbm` | 199 | | **CatBoost** | Gradient boosting library excelling with categorical features (minimal preprocessing needed). Robust against overfitting. | [Docs](https://catboost.ai/docs/) • [GitHub](https://github.com/catboost/catboost) | `pip install catboost` | 200 | | **NGBoost** | Extends gradient boosting to probabilistic prediction, providing uncertainty estimates alongside point predictions. Built on scikit-learn. | [Docs](https://stanfordmlgroup.github.io/ngboost/) • [GitHub](https://github.com/stanfordmlgroup/ngboost) | `pip install ngboost` | 201 | 202 | *(Note: Scikit-learn also provides GradientBoostingClassifier/Regressor. XGBoost and LightGBM are primarily Gradient Boosting but offer Random Forest modes.)* 203 | 204 | --- 205 | 206 | ## Time Series Forecasting 207 | 208 | Packages focused on predicting future values of time series, including classical models, machine learning approaches, and specialized forecasting frameworks. 209 | 210 | | Package | Description | Links | Installation | 211 | |------------------|-------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|------------------------| 212 | | **Statsmodels** | Core implementations of classical forecasting models like ARIMA, SARIMAX, Exponential Smoothing (ETS), Unobserved Components (UCM). | [Docs (TSA)](https://www.statsmodels.org/stable/tsa.html) • [GitHub](https://github.com/statsmodels/statsmodels) | `pip install statsmodels`| 213 | | **pmdarima** | ARIMA modeling with automatic parameter selection (auto-ARIMA), similar to R's `forecast::auto.arima`. | [Docs](https://alkaline-ml.com/pmdarima/) • [GitHub](https://github.com/alkaline-ml/pmdarima) | `pip install pmdarima` | 214 | | **Prophet** | Forecasting procedure for time series with strong seasonality and trend components, developed by Facebook. | [Docs](https://facebook.github.io/prophet/) • [GitHub](https://github.com/facebook/prophet) | `pip install prophet` | 215 | | **StatsForecast**| Fast, scalable implementations of popular statistical forecasting models (ETS, ARIMA, Theta, etc.) optimized for performance. | [Docs](https://nixtla.github.io/statsforecast/) • [GitHub](https://github.com/Nixtla/statsforecast) | `pip install statsforecast`| 216 | | **MLForecast** | Scalable time series forecasting using machine learning models (e.g., LightGBM, XGBoost) as regressors. | [Docs](https://nixtla.github.io/mlforecast/) • [GitHub](https://github.com/Nixtla/mlforecast) | `pip install mlforecast` | 217 | | **NeuralForecast**| Deep learning models (N-BEATS, N-HiTS, Transformers, RNNs) for time series forecasting, built on PyTorch Lightning. | [Docs](https://nixtla.github.io/neuralforecast/) • [GitHub](https://github.com/Nixtla/neuralforecast) | `pip install neuralforecast`| 218 | | **sktime** | Unified framework for various time series tasks, including forecasting with classical, ML, and deep learning models. | [Docs](https://www.sktime.net/en/latest/) • [GitHub](https://github.com/sktime/sktime) | `pip install sktime` | 219 | 220 | --- 221 | 222 | ## Time Series Econometrics 223 | 224 | Libraries focused on modeling dynamic relationships, causality, conditional volatility, and structural properties in time series data using methods like VAR, VECM, GARCH, and impulse response analysis. 225 | 226 | | Package | Description (Focus) | Links | Installation | 227 | |---------------------|---------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------|--------------------------| 228 | | **Statsmodels** | Implements Vector Autoregression (VAR), SVAR, VECM, impulse response functions, Granger causality tests, unit root/cointegration tests. | [Docs (VAR)](https://www.statsmodels.org/stable/vector_ar.html) • [Docs (Tests)](https://www.statsmodels.org/stable/tsa.html#statistical-tests) • [GitHub](https://github.com/statsmodels/statsmodels) | `pip install statsmodels` | 229 | | **ARCH** | Specialized library for modeling and forecasting conditional volatility using ARCH, GARCH, EGARCH, and related models. | [Docs](https://arch.readthedocs.io/) • [GitHub](https://github.com/bashtage/arch) | `pip install arch` | 230 | | **LocalProjections**| Community implementations of Jordà (2005) Local Projections for estimating impulse responses without VAR assumptions. | [Example GitHub](https://github.com/elenev/localprojections) | Install from source | 231 | | **Kats** | Broad toolkit for time series analysis, including multivariate analysis, detection (outliers, change points, trends), feature extraction. | [Docs](https://facebookresearch.github.io/Kats/) • [GitHub](https://github.com/facebookresearch/Kats) | `pip install kats` | 232 | 233 | --- 234 | 235 | ## State Space & Volatility Models 236 | 237 | Libraries for representing and estimating models in state-space form (using Kalman filtering/smoothing) and for modeling stochastic volatility. 238 | 239 | | Package | Description (Focus) | Links | Installation | 240 | |---------------------|------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------|----------------------------| 241 | | **Statsmodels** | Comprehensive state-space modeling framework (`tsa.statespace`) supporting ARIMA, VARMAX, structural time series (UCM), DFM, custom models. | [Docs (StateSpace)](https://www.statsmodels.org/stable/statespace.html) • [GitHub](https://github.com/statsmodels/statsmodels) | `pip install statsmodels` | 242 | | **stochvol** | Efficient Bayesian estimation of stochastic volatility (SV) models using MCMC. | [Docs](https://stochvol.readthedocs.io/en/latest/) • [GitHub](https://github.com/rektory/stochvol) | `pip install stochvol` | 243 | | **Metran** | Specialized package for estimating Dynamic Factor Models (DFM) using state-space methods and Kalman filtering. | [GitHub](https://github.com/pastas/metran) | `pip install metran` | 244 | | **FilterPy** | Focuses on Kalman filters (standard, EKF, UKF) and smoothers with a clear, pedagogical implementation style. | [Docs](https://filterpy.readthedocs.io/en/latest/) • [GitHub](https://github.com/rlabbe/filterpy) | `pip install filterpy` | 245 | | **PyKalman** | Implements Kalman filter, smoother, and EM algorithm for parameter estimation, including support for missing values and UKF. | [PyPI](https://pypi.org/project/pykalman/) • [GitHub](https://github.com/pykalman/pykalman) | `pip install pykalman` | 246 | | **PyMC Statespace** | (See Bayesian) Bayesian state-space modeling using PyMC, integrating Kalman filtering within MCMC for parameter estimation. | [Docs](https://pymc-statespace.readthedocs.io/en/latest/) • [GitHub](https://github.com/pymc-devs/pymc-statespace) (Note: Check repo status) | `pip install pymc-statespace`| 247 | 248 | --- 249 | 250 | ## High-Dimensional Methods 251 | 252 | Techniques for estimation and inference when the number of parameters is large relative to the sample size. 253 | 254 | | Package | Description | Links | Installation | 255 | |--------------------------|----------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|--------------------------------------| 256 | | **Scikit-learn** | Implements regularized regression methods like Lasso, Ridge, and ElasticNet for sparse modeling/feature selection. | [Docs](https://scikit-learn.org/stable/modules/linear_model.html) • [GitHub](https://github.com/scikit-learn/scikit-learn) | `pip install scikit-learn` | 257 | | **PyFixest** | Efficiently handles high-dimensional fixed effects in linear models (see Panel Data section). | [Docs & GitHub](https://github.com/py-econometrics/pyfixest) | `pip install pyfixest` | 258 | | **FixedEffectModelPyHDFE** | Solves linear models with high-dimensional fixed effects (see Panel Data section). | [PyPI](https://pypi.org/project/FixedEffectModelPyHDFE/) | `pip install FixedEffectModelPyHDFE` | 259 | 260 | --- 261 | 262 | ## Discrete Choice Models 263 | 264 | Packages for analyzing choice behavior, demand estimation, and modeling qualitative dependent variables. 265 | 266 | | Package | Description | Links | Installation | 267 | |-----------------|-------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|--------------------------| 268 | | **Statsmodels** | Includes Logit, Probit, Multinomial Logit (MNLogit), and Conditional Logit models. | [Docs](https://www.statsmodels.org/stable/discretemod.html) • [GitHub](https://github.com/statsmodels/statsmodels) | `pip install statsmodels` | 269 | | **XLogit** | Fast estimation of Multinomial Logit and Mixed Logit models, optimized for performance. | [Docs](https://xlogit.readthedocs.io/) • [GitHub](https://github.com/arteagac/xlogit) | `pip install xlogit` | 270 | | **PyLogit** | Flexible implementation of conditional/multinomial logit models with utilities for data preparation. | [GitHub](https://github.com/timothyb0912/pylogit) | `pip install pylogit` | 271 | | **PyBLP** | Tools for estimating demand for differentiated products using the Berry-Levinsohn-Pakes (BLP) method. | [Docs](https://pyblp.readthedocs.io/) • [GitHub](https://github.com/jeffgortmaker/pyblp) | `pip install pyblp` | 272 | | **torch-choice**| PyTorch framework for flexible estimation of complex discrete choice models, leveraging GPU acceleration. | [Docs](https://gsbdbi.github.io/torch-choice/) • [GitHub](https://github.com/gsbDBI/torch-choice) | `pip install torch-choice`| 273 | | **Biogeme** | Maximum likelihood estimation of parametric models, with strong support for complex discrete choice models. | [Docs](https://biogeme.epfl.ch/index.html) • [GitHub](https://github.com/michelbierlaire/biogeme) | `pip install biogeme` | 274 | 275 | --- 276 | 277 | ## Structural Econometrics & Estimation 278 | 279 | Frameworks for specifying, simulating, and estimating structural economic models, often involving dynamic programming or complex likelihoods. 280 | 281 | | Package | Description (Focus) | Links | Installation | 282 | |---------------------|-----------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|-----------------------| 283 | | **respy** | Simulation and estimation of finite-horizon dynamic discrete choice (DDC) models (e.g., labor/education choice). | [Docs](https://respy.readthedocs.io/en/latest/) • [GitHub](https://github.com/OpenSourceEconomics/respy) | `pip install respy` | 284 | | **HARK** | Toolkit for solving, simulating, and estimating models with heterogeneous agents (e.g., consumption-saving). | [Docs](https://hark.readthedocs.io/en/latest/) • [GitHub](https://github.com/econ-ark/HARK) | `pip install econ-ark` | 285 | | **Dolo** | Framework for describing and solving economic models (DSGE, OLG, etc.) using a declarative YAML-based format. | [Docs](https://dolo.readthedocs.io/en/latest/) • [GitHub](https://github.com/EconForge/dolo) | `pip install dolo` | 286 | | **PyBLP** | (See Discrete Choice) Estimation of demand using Berry-Levinsohn-Pakes (BLP) structural models. | [Docs](https://pyblp.readthedocs.io/) • [GitHub](https://github.com/jeffgortmaker/pyblp) | `pip install pyblp` | 287 | | **QuantEcon.py** | Core library for quantitative economics: dynamic programming, Markov chains, game theory, numerical methods. | [Docs](https://quantecon.org/python-lectures/) • [GitHub](https://github.com/QuantEcon/QuantEcon.py) | `pip install quantecon` | 288 | 289 | --- 290 | 291 | ## Quantile Regression & Distributional Methods 292 | 293 | Methods for modeling the conditional quantiles or the entire conditional distribution of an outcome variable. 294 | 295 | | Package | Description | Links | Installation | 296 | |--------------------|-------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|--------------------------| 297 | | **Statsmodels** | Provides an implementation of quantile regression. | [Docs](https://www.statsmodels.org/stable/generated/statsmodels.regression.quantile_regression.QuantReg.html) • [GitHub](https://github.com/statsmodels/statsmodels) | `pip install statsmodels` | 298 | | **pyqreg** | Fast quantile regression solver using interior point methods, supporting robust and clustered standard errors. | [Docs & GitHub](https://github.com/mozjay0619/pyqreg) | `pip install pyqreg` | 299 | | **quantile-forest**| Scikit-learn compatible implementation of Quantile Regression Forests for non-parametric estimation. | [Docs](https://zillow.github.io/quantile-forest/) • [GitHub](https://github.com/zillow/quantile-forest) | `pip install quantile-forest`| 300 | | **pyrifreg** | Recentered Influence‑Function (RIF) regression for unconditional quantile & distributional effects (Firpo et al., 2008). | [Docs & GitHub](https://github.com/vyasenov/pyrifreg) | `pip install pyrifreg` | 301 | 302 | --- 303 | 304 | ## Bayesian Econometrics 305 | 306 | Packages for performing Bayesian inference and probabilistic modeling. 307 | 308 | | Package | Description | Links | Installation | 309 | |-----------------|----------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------|----------------------| 310 | | **PyMC** | Flexible probabilistic programming library for Bayesian modeling and inference using MCMC algorithms (NUTS). | [Docs](https://www.pymc.io/) • [GitHub](https://github.com/pymc-devs/pymc) | `pip install pymc` | 311 | | **Bambi** | High-level interface for building Bayesian GLMMs, built on top of PyMC. Uses formula syntax similar to R's `lme4`. | [Docs](https://bambinos.github.io/bambi/) • [GitHub](https://github.com/bambinos/bambi) | `pip install bambi` | 312 | | **NumPyro** | Probabilistic programming library built on JAX for scalable Bayesian inference, often faster than PyMC. | [Docs](https://num.pyro.ai/) • [GitHub](https://github.com/pyro-ppl/numpyro) | `pip install numpyro`| 313 | | **LightweightMMM**| Bayesian Marketing Mix Modeling (see Marketing Mix Models section). | [GitHub](https://github.com/google/lightweight_mmm) | `pip install lightweight_mmm`| 314 | 315 | --- 316 | 317 | ## Marketing Mix Models (MMM) & Business Analytics 318 | 319 | Specialized packages for attributing marketing impact, customer analytics (CLV), and optimizing spend. 320 | 321 | | Package | Description | Links | Installation / Access | 322 | |------------------|------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|------------------------------| 323 | | **LightweightMMM** | Google's Bayesian approach to Marketing Mix Modeling focusing on channel attribution and budget optimization. | [Docs](https://lightweight-mmm.readthedocs.io) • [GitHub](https://github.com/google/lightweight_mmm) | `pip install lightweight_mmm` | 324 | | **PyMC Marketing**| Collection of Bayesian marketing models built with PyMC, including MMM, CLV, and attribution. | [Docs](https://www.pymc-marketing.io/) • [GitHub](https://github.com/pymc-labs/pymc-marketing) | `pip install pymc-marketing` | 325 | | **MaMiMo** | Lightweight Python library focused specifically on Marketing Mix Modeling implementation. | [GitHub](https://github.com/Garve/mamimo) | `pip install mamimo` | 326 | | **mmm_stan** | Python/STAN implementation of Bayesian Marketing Mix Models. | [GitHub](https://github.com/sibylhe/mmm_stan) | GitHub Repository | 327 | | **Lifetimes** | Analyze customer lifetime value (CLV) using probabilistic models (BG/NBD, Pareto/NBD) to predict purchases. | [Docs](https://lifetimes.readthedocs.io/en/latest/) • [GitHub](https://github.com/CamDavidsonPilon/lifetimes) | `pip install lifetimes` | 328 | 329 | --- 330 | 331 | ## Spatial Econometrics 332 | 333 | Tools for analyzing data with spatial dependencies or geographic structure. 334 | 335 | | Package | Description | Links | Installation | 336 | |-----------------|------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------|-----------------------------| 337 | | **PySAL (spreg)**| The spatial regression `spreg` module of PySAL. Implements spatial lag, error, IV models, and diagnostics. | [Docs](https://pysal.org/spreg/) • [GitHub](https://github.com/pysal/spreg) | `pip install spreg` | 338 | | *(PySAL Core)* | The broader PySAL ecosystem contains many tools for spatial data handling, weights, visualization, and analysis. | [Docs](https://pysal.org/) • [GitHub](https://github.com/pysal/pysal) | `pip install pysal` | 339 | 340 | --- 341 | 342 | ## Dimensionality Reduction 343 | 344 | Libraries for reducing the number of variables in a dataset while preserving important information, including linear methods (PCA, Factor Analysis) and non-linear manifold learning techniques (t-SNE, UMAP). 345 | 346 | | Package | Description (Focus) | Links | Installation | 347 | |---------------------|----------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------|--------------------------| 348 | | **Scikit-learn** | Foundational ML library with various dimensionality reduction methods: PCA, Factor Analysis, Isomap, t-SNE, etc. | [Docs](https://scikit-learn.org/stable/modules/unsupervised_reduction.html) • [GitHub](https://github.com/scikit-learn/scikit-learn) | `pip install scikit-learn` | 349 | | **FactorAnalyzer** | Specialized library for Exploratory (EFA) and Confirmatory (CFA) Factor Analysis with rotation options for interpretability. | [Docs](https://factor-analyzer.readthedocs.io/en/latest/) • [GitHub](https://github.com/EducationalTestingService/factor_analyzer) | `pip install factor_analyzer`| 350 | | **umap-learn** | Fast and scalable implementation of Uniform Manifold Approximation and Projection (UMAP) for non-linear reduction. | [Docs](https://umap-learn.readthedocs.io/en/latest/) • [GitHub](https://github.com/lmcinnes/umap) | `pip install umap-learn` | 351 | | **openTSNE** | Optimized, parallel implementation of t-distributed Stochastic Neighbor Embedding (t-SNE) for large datasets. | [Docs](https://opentsne.readthedocs.io/en/stable/) • [GitHub](https://github.com/pavlin-policar/openTSNE) | `pip install opentsne` | 352 | 353 | --- 354 | 355 | ## Natural Language Processing for Economics 356 | 357 | Libraries for processing and analyzing textual data within economic contexts. 358 | 359 | | Package | Description | Links | Installation | 360 | |----------------------------|--------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|-----------------------------| 361 | | **Transformers**| Access to thousands of pre-trained models for NLP tasks like text classification, summarization, embeddings, etc. | [Docs](https://huggingface.co/transformers/) • [GitHub](https://github.com/huggingface/transformers) | `pip install transformers` | 362 | | **Gensim** | Library focused on topic modeling (LDA, LSI) and document similarity analysis. | [Docs](https://radimrehurek.com/gensim/) • [GitHub](https://github.com/RaRe-Technologies/gensim) | `pip install gensim` | 363 | | **spaCy** | Industrial-strength NLP library for efficient text processing pipelines (NER, POS tagging, etc.). | [Docs](https://spacy.io/) • [GitHub](https://github.com/explosion/spaCy) | `pip install spacy` | 364 | 365 | --- 366 | 367 | ## Synthetic Data Generation 368 | 369 | Tools for creating artificial datasets that mimic the statistical properties of real-world data. 370 | 371 | | Package | Description | Links | Installation | 372 | |------------------------------|---------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------|--------------------------| 373 | | **SDV (Synthetic Data Vault)** | Comprehensive library for generating synthetic tabular, relational, and time series data using various models. | [Docs](https://sdv.dev/) • [GitHub](https://github.com/sdv-dev/SDV) | `pip install sdv` | 374 | | **Synthpop** | Port of the R package for generating synthetic populations based on sample survey data. | [GitHub](https://github.com/alan-turing-institute/synthpop) | `pip install synthpop` | 375 | 376 | --- 377 | 378 | ## Numerical Optimization & Computational Tools 379 | 380 | Foundational libraries for numerical computation, automatic differentiation, and optimization. 381 | 382 | | Package | Description | Links | Installation | 383 | |-----------|-----------------------------------------------------------------------------------------------|---------------------------------------------------------------------------|----------------------| 384 | | **JAX** | High-performance numerical computing with autograd and XLA compilation on CPU/GPU/TPU. | [Docs](https://jax.readthedocs.io/) • [GitHub](https://github.com/google/jax) | `pip install jax` | 385 | | **PyTorch**| Popular deep learning framework with flexible automatic differentiation. | [Docs](https://pytorch.org/) • [GitHub](https://github.com/pytorch/pytorch) | (See PyTorch website)| 386 | 387 | --- 388 | 389 | ## Standard Errors, Bootstrapping & Reporting 390 | 391 | Tools for robust statistical inference (corrected standard errors, bootstrapping) and creating publication-quality regression tables. 392 | 393 | | Package | Description (Focus) | Links | Installation | 394 | |-------------------------|--------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------|------------------------------| 395 | | **Statsmodels** | Core library for estimating robust standard errors (HC0-HC3, HAC, Clustered) via `get_robustcov_results`. Provides building blocks for manual bootstrap. | [Docs (Robust SE)](https://www.statsmodels.org/stable/regression.html#robust-standard-errors) • [GitHub](https://github.com/statsmodels/statsmodels) | `pip install statsmodels` | 396 | | **wildboottest** | Fast implementation of various wild cluster bootstrap algorithms (WCR, WCU) for robust inference, especially with few clusters. | [Docs](https://py-econometrics.github.io/wildboottest/) • [GitHub](https://github.com/py-econometrics/wildboottest) | `pip install wildboottest` | 397 | | **SciPy Bootstrap** | (`scipy.stats.bootstrap`) Computes bootstrap confidence intervals for various statistics using percentile, BCa methods. | [Docs](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.bootstrap.html) • [GitHub (SciPy)](https://github.com/scipy/scipy) | `pip install scipy` | 398 | | **Stargazer** | Python port of R's stargazer for creating publication-quality regression tables (HTML, LaTeX) from `statsmodels` & `linearmodels` results. | [GitHub](https://github.com/StatsReporting/stargazer) | `pip install stargazer` | 399 | 400 | --- 401 | 402 | ## Learning Resources 403 | 404 | Curated resources for learning econometrics and quantitative economics with Python. 405 | 406 | | Resource | Description | Link | 407 | |------------------------------|-----------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------| 408 | | **QuantEcon Lectures** | High-quality lecture series on quantitative economic modeling, computational tools, and economics using Python/Julia. | [Website](https://quantecon.org/lectures/) | 409 | | **Python for Econometrics** | Comprehensive intro notes by Kevin Sheppard covering Python basics, core libraries, and econometrics applications. | [PDF](https://www.kevinsheppard.com/files/teaching/python/notes/python_introduction_2023.pdf) | 410 | | **Causal Inference for the Brave and True**| Modern introduction to causal inference methods (DiD, IV, RDD, Synth, ML-based) with Python code examples. | [Website](https://matheusfacure.github.io/python-causality-handbook/) | 411 | | **Coding for Economists** | Practical guide by A. Turrell on using Python for modern econometric research, data analysis, and workflows. | [Website](https://aeturrell.github.io/coding-for-economists/) | 412 | | **The Missing Semester of Your CS Education (MIT)** | Teaches essential developer tools often skipped in formal education—command line, Git, Vim, scripting, debugging, etc. | [Website](https://missing.csail.mit.edu/) | 413 | | **Machine Learning Specialization (Coursera)** | Beginner-friendly 3-course series by Andrew Ng covering core ML methods (regression, classification, clustering, trees, NN) with hands-on projects. | [Course](https://www.coursera.org/specializations/machine-learning-introduction/) | 414 | | **Deep Learning Specialization (Coursera)** | Intermediate 5-course series by Andrew Ng covering deep neural networks, CNNs, RNNs, transformers, and real-world DL applications using TensorFlow. | [Course](https://www.coursera.org/specializations/deep-learning) | 415 | | **Beyond Jupyter (TransferLab)** | Teaches software design principles for ML—modularity, abstraction, and reproducibility—going beyond ad hoc Jupyter workflows. Focus on maintainable, production-quality ML code. | [Website](https://transferlab.ai/trainings/beyond-jupyter/) | 416 | | **Awesome Quant** | Curated list of quantitative finance libraries and resources (many statistical/TS tools overlap with econometrics). | [Website](https://wilsonfreitas.github.io/awesome-quant/) | 417 | 418 | --- 419 | 420 | ## Contributing 421 | 422 | Contributions are welcome! Please refer to the `CONTRIBUTING.md` file (linked via the badge at the top) in the repository root for details on how to: 423 | 424 | 1. **Add new packages**: Submit a pull request with relevant, well-maintained packages. 425 | 2. **Improve documentation**: Help enhance descriptions, add examples, or correct errors. 426 | 3. **Report issues**: Notify us about broken links, outdated information, or suggest new categories. 427 | 428 | *Last updated: June 2025* 429 | --------------------------------------------------------------------------------