Tools and standards for conducting and evaluating research in software engineering

Make AI agents and AI workflows measurably reliable. Epistemic measurement, Noetic RAG, Sentinel gating, and grounded calibration for Claude Code and beyond

Introduction to Empirical Bayes: Examples from Baseball Statistics

Python library that represents empirical distribution functions.

A language for time-series analysis

Test and evaluate LLMs and model configurations, across all the scenarios that matter for your application

A collection of empirical experiments using large language models and other neural network architectures to test the usefulness of metaphysical constructs.

Homepage for 17-803 "Empirical Methods" at Carnegie Mellon University

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Repositório informativo com diretrizes empíricas para o desenvolvimento de um Projeto Web.

A library of tools for scientific software development, with emphasis on also being able to build web interfaces using Emscripten.

A collection implementing core statistical concepts—probability, distributions, limit theorems, hypothesis testing, and regression—through statistical computing and data visualization with R.

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Open source project to tackle the problem of long development cycles required to produce software to conduct multi-participant and real-time human experiments online.

Simple client-server application for generating user-defined traffic patterns.

Artifact repository for the paper "Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code", In Proceedings of The 46th IEEE/ACM International Conference on Software Engineering (ICSE 2024), Lisbon, Portugal, April 2024

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Limitations of the Empirical Fisher Approximation

Measure time complexity empirically.

Winter 2020 Course description: Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlations, stability, regressions, and statistical inference using financial time series. Topic 1: Time series properties of stock market returns and prices  Class intro: Forecasting and Finance  The random walk hypothesis  Stationarity  Time-varying volatility and General Least Squares  Robust standard errors and OLS Topic 2: Time-dependence and predictability  ARMA models  The likelihood function, exact and conditional likelihood estimation  Predictive regressions, autocorrelation robust standard errors  The Campbell-Shiller decomposition  Present value restrictions  Multivariate analysis: Vector Autoregression (VAR) models, the Kalman Filter Topic 3: Heteroscedasticity  Time-varying volatility in the data  Realized Variance  ARCH and GARCH models, application to Value-at-Risk Topic 4: Time series properties of the cross-section of stock returns  Single- and multifactor models  Economic factors: Models and data exploration  Statistical factors: Principal Components Analysis  Fama-MacBeth regressions and characteristics-based factors

Machine learning methods for identifing investment factors

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A PyTorch implementation of EmpiricalMVM

Empirical Finance Course (PhD, Julia code)

Collection of published papers that estimate dynamic programming models

Fama-French models, idiosyncratic volatility, event study

Online empirical cumulative distribution functions

Empirical Core is the mono repo for the Quill.org codebase. This repo contains both the Quill Learning Tools, such as Quill Connect, and the Quill Learning Management System, the platform that enables teachers to provide assignments and view results. (Empirical is the name of the nonprofit building Quill.org).

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