Search Results for "regression" - Page 2
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Showing 313 open source projects for "regression"
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ShinyItemAnalysis
Test and Item Analysis via Shiny
...Exploration of total and standard scores. Analysis of measurement error and reliability. Analysis of correlation structure and validity. Traditional item analysis. Item analysis with regression models. Item analysis with IRT models. Detection of differential item functioning. Number of toy datasets is available, the interactive application also allows the users to upload and analyze their own data and to automatically generate PDF or HTML reports. All methods include sample R code which is ready to copy and paste into R and run locally. ... -
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PySINDy
A package for the sparse identification of nonlinear dynamical systems
...This approach is particularly valuable in scientific fields such as physics, engineering, and biology where researchers seek both predictive accuracy and theoretical insight. The library provides tools for constructing libraries of candidate functions, performing sparse regression, and validating discovered models against observed data. It integrates with standard Python scientific computing libraries, making it easy to apply to experimental datasets or simulated systems. -
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React Screenshot Test
A dead simple library to screenshot test React components
A dead simple library to screenshot test React components. Under the hood, we start a local server that renders components server-side. Each component is given its own dedicated page (e.g. /render/my-component). Then we use Puppeteer to take a screenshot of that page. If you work on a team where developers use a different OS (e.g. Mac OS and Linux), or if you develop on Mac OS but use Linux for continuous integration, you would quickly run into issues where screenshots are inconsistent... -
4
dlib
Toolkit for making machine learning and data analysis applications
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge. Good unit test coverage, the ratio of unit test lines of code to library lines of code is... -
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5
Agentless
An agentless approach to automatically solve software development
...It then generates multiple candidate patches for the identified locations using language model reasoning and diff-style edits. In the final stage, the framework validates potential patches by running regression tests and additional reproduction tests to confirm whether the fix resolves the original error. Based on these results, the system ranks the candidate patches and selects the most reliable solution to submit. -
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mlr3
mlr3: Machine Learning in R - next generation
...It provides core abstractions (tasks, learners, resamplings, measures, pipelines) implemented using R6 classes, enabling extensible, composable machine learning workflows. It focuses on clean design, scalability (large datasets), and integration into the wider R ecosystem via extension packages. Users can do classification, regression, survival analysis, clustering, hyperparameter tuning, benchmarking etc., often via companion packages. -
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nuclei
Fast and customizable vulnerability scanner based on simple YAML
Nuclei is used to send requests across targets based on a template, leading to zero false positives and providing fast scanning on a large number of hosts. Nuclei offers scanning for a variety of protocols, including TCP, DNS, HTTP, SSL, File, Whois, Websocket, Headless etc. With powerful and flexible templating, Nuclei can be used to model all kinds of security checks. We have a dedicated repository that houses various type of vulnerability templates contributed by more than 300 security... -
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Homemade Machine Learning
Python examples of popular machine learning algorithms
...Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is pedagogical: you’ll see linear regression, logistic regression, k-means clustering, neural nets, decision trees, etc., built in Python using fundamentals like NumPy and Matplotlib, not hidden behind API calls. It is well suited for learners who want to move beyond library usage to understand how algorithms operate internally—how cost functions, gradients, updates and predictions work. -
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ClusterFuzz
Scalable fuzzing infrastructure
ClusterFuzz is a scalable fuzzing infrastructure that finds security and stability issues in software. Google uses ClusterFuzz to fuzz all Google products and as the fuzzing backend for OSS-Fuzz. ClusterFuzz provides many features which help seamlessly integrate fuzzing into a software project's development process. Can run on any size cluster (e.g. OSS-Fuzz instance runs on 100,000 VMs). Fully automatic bug filing, triage and closing for various issue trackers (e.g. Monorail, Jira).... -
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AG KIT
Antigravity-first agent engineering kit with rules, skills
...A native pre-tool safety gate blocks high-confidence destructive commands without interfering with normal project cleanup. Merge-aware updates preserve local changes, create backups, and support rollback. Built-in validation, doctor checks, regression tests, and reproducible manifests help teams maintain reliable agent configurations. -
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Halfrost-Field Frostland
This is the place to blog
...The repository is structured like a personal technical blog/book: it contains “contents” directories with Markdown-based notes, tutorials and guides. For example, there is a full machine learning course outline (regression, neural networks, SVMs, unsupervised learning, anomaly detection, large-scale ML, even application examples like OCR), that reads like a self-study curriculum. Beyond ML, the repo reflects the author’s interests across cloud native infra, distributed systems, programming languages (Go, Rust), DevOps, algorithms, and more — making it a broad reference for learners or engineers seeking well-written, deep-dive articles. -
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gtsummary
Presentation-Ready Data Summary and Analytic Result Tables
gtsummary is an R package for creating elegant, customizable, publication-ready summary tables of datasets and statistical models. It provides concise code to produce demographic tables (tbl_summary()), regression result tables, and more, with flexible styling options for reporting. -
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ml.js
Machine learning tools in JavaScript
This library is a compilation of the tools developed in the mljs organization. It is mainly maintained for use in the browser. If you are working with Node.js, you might prefer to add to your dependencies only the libraries that you need, as they are usually published to npm more often. We prefix all our npm package names with ml- (eg. ml-matrix) so they are easy to find. -
14
Future AGI
Open-source platform for evaluating, observing, and improving LLM
...It supports both cloud and self-hosted deployment models, making it useful for teams with different privacy, infrastructure, and compliance needs. Future AGI is especially relevant for agent-heavy products where reliability, regression testing, and safety checks matter before and after release. Its main value is turning AI agent development into a measurable engineering process instead of an informal cycle of prompting, guessing, and manual review. -
15
Robyn
Experimental, AI/ML-powered and open sourced Marketing Mix Modeling
...Robyn takes in historical data (spends on different marketing channels, conversions, or revenue, and optional context or organic-media variables) and uses a combination of techniques, regularized regression (Ridge), time-series decomposition (trend, seasonality, holiday effects), and hyperparameter optimization (via evolutionary algorithms), to estimate the incremental impact of each marketing channel. It explicitly models “carry-over” (adstock) and diminishing-returns (saturation) effects per channel, enabling realistic modeling of how advertising persists over time and saturates. -
16
NGBoost
Natural Gradient Boosting for Probabilistic Prediction
ngboost is a Python library that implements Natural Gradient Boosting, as described in "NGBoost: Natural Gradient Boosting for Probabilistic Prediction". It is built on top of Scikit-Learn and is designed to be scalable and modular with respect to the choice of proper scoring rule, distribution, and base learner. A didactic introduction to the methodology underlying NGBoost is available in this slide deck. -
17
Better Shot
An open-source alternative to CleanShot X for macOS
...Instead of relying on third-party screenshot APIs or browser extensions, it runs a local or server-deployed service that generates high-quality renders on demand, making it ideal for automated documentation, preview panes, social sharing cards, or visual regression tasks. Better-Shot can take full-page captures, custom viewport shots, or even multi-step snapshots that include dynamic content generated by JavaScript, ensuring accurate representation of modern web applications. It supports configuration options for resolution, user agent, cookies, and authentication, so you can tailor captures for staging, authenticated dashboards, or custom deployments. -
18
BayesianOptimization
A Python implementation of global optimization with gaussian processes
BayesianOptimization is a Python library that helps find the maximum (or minimum) of expensive or unknown objective functions using Bayesian optimization. This technique is especially useful for hyperparameter tuning in machine learning, where evaluating the objective function is costly. The library provides an easy-to-use API for defining bounds and optimizing over parameter spaces using probabilistic models like Gaussian Processes. -
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AccessibilitySnapshot
Easy regression testing for iOS accessibility
AccessibilitySnapshot is an iOS development tool that enables snapshot testing of accessibility elements in UIKit apps. It helps developers ensure that accessibility labels, traits, and hierarchies are properly configured and presented. By providing automated testing capabilities, it improves accessibility compliance and makes UI testing more robust. -
20
statsmodels
Statsmodels, statistical modeling and econometrics in Python
statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. Generalized linear models with support for all... -
21
Python 100 Days
Python - From Novice to Master in 100 Days
...Data analysis and visualization receive dedicated coverage via NumPy, pandas, matplotlib, seaborn, and pyecharts, followed by an applied machine learning track with kNN, trees, Bayes, regression, clustering, ensembles, and neural networks. -
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Aerosolve
A machine learning package built for humans
Aerosolve is an open-source machine learning library developed by Airbnb, designed for interpretable and human-friendly modeling. Built around sparse, human-intuitive features (like geography, pricing), it supports feature quantization, interaction specification, and rule-based priors—enabling domain experts to contribute directly to model behavior. -
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sqlsmith
A random SQL query generator
SQLSmith is a fuzz testing tool for PostgreSQL that automatically generates random SQL queries to uncover bugs in the query planner and executor. It is widely used by PostgreSQL developers and database vendors to stress-test SQL features and engine behavior under edge-case conditions. SQLSmith helps improve database robustness by revealing unexpected failures. -
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ML Sharp
Sharp Monocular View Synthesis in Less Than a Second
ML Sharp is a research code release that turns a single 2D photograph into a photorealistic 3D representation that can be rendered from nearby viewpoints. Instead of requiring multi-view input, it predicts the parameters of a 3D Gaussian scene representation directly from one image using a single forward pass through a neural network. The core idea is speed: the 3D representation is produced in under a second on a standard GPU, and then the resulting scene can be rendered in real time to... -
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FlowLens MCP
Open-source MCP server that gives your coding agent
FlowLens MCP Server is an open-source tool designed to give AI-powered coding agents (like Claude Code, Cursor, GitHub Copilot / Codex, and others) full, replayable browser context to dramatically improve debugging, bug reporting, and regression testing for web applications. It works together with a companion browser extension: when a user reproduces a bug or a complicated UI interaction, the extension captures a rich session log, including screen/video recording, network traffic, console logs, DOM events, storage changes, and more, and exports it. The MCP server then loads this captured “flow” and exposes it to the AI agent via the Model Context Protocol (MCP), letting the agent examine, search, filter, and reason about the session just as a human developer would, without needing the agent to re-run the flow or rely on minimal reproduction data (logs, screenshots).
