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Showing 290 open source projects for "estimate"
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Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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Bayes+Estimate is a Rust and C++ library that implement numerical algorithms for Bayesian estimation. They provide tested and consistent numerical methods and represents the wide variety of Bayesian estimation algorithms and system model.
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Infracost VSCode Extension
See cost estimates for Terraform right in your editor
...Prevent costly infrastructure changes before they get into production. Compare configs, instance types, regions etc: copy/paste a code block, make changes and compare them. Quick cost estimate: write a code block and get a cost estimate without having to use AWS, Azure or Google cost calculators, or read the long/complicated pricing web pages. Catch costly typos: if you accidentally type 22 instead of 2 as the instance count, or 1000GB volume size instead of 100, the cost estimate will immediately pick that up and let you know. ... -
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Kepler
Kepler (Kubernetes-based Efficient Power Level Exporter)
Kepler (Kubernetes-based Efficient Power Level Exporter) uses eBPF to probe performance counters and other system stats, use ML models to estimate workload energy consumption based on these stats, and exports them as Prometheus metrics. -
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ProbNumDiffEq.jl
Probabilistic Numerical Differential Equation solvers via Bayesian fil
ProbNumDiffEq.jl provides probabilistic numerical ODE solvers to the DifferentialEquations.jl ecosystem. The implemented ODE filters solve differential equations via Bayesian filtering and smoothing. The filters compute not just a single point estimate of the true solution, but a posterior distribution that contains an estimate of its numerical approximation error. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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blavaan
An R package for Bayesian structural equation modeling
blavaan is a free, open-source R package for Bayesian latent variable analysis. It relies on JAGS and Stan to estimate models via MCMC. The blavaan functions and syntax are similar to lavaan. The development version of blavaan (containing updates not yet on CRAN) can be installed via the command provided in the documentation. Compilation is required; this may be a problem for users who currently rely on a binary version of blavaan from CRAN. The blavaan package depends on the lavaan package for model specification and for some computations. ... -
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MESHROOM
3D reconstruction software
...The dense modeling of the scene is the result yielded by chaining two computer vision-based pipelines, “Structure-from-Motion” (SfM) and “Multi View Stereo” (MVS). Fusion of Multi-bracketing LDR images into HDR. Alignment of panorama images. Support for fisheye optics. Automatically estimate fisheye circle or manually edit it. Take advantage of motorized-head file. Easy to integrate in your Renderfarm System. Add specific rules to select the most suitable machines regarding CPU, RAM, GPU requirements of each Node. -
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NannyML
Detecting silent model failure. NannyML estimates performance
...When the actual outcome of your deployed prediction models is delayed, or even when post-deployment target labels are completely absent, you can use NannyML's CBPE-algorithm to estimate model performance. -
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Infracost
Cloud cost estimates for Terraform in pull requests
...Infracost supports over 230 Terraform resources across AWS, Azure and Google. Other IaC tools, such as Pulumi, AWS CloudFormation/CDK and Azure ARM/Bicep are on our roadmap. Infracost can also estimate usage-based resources such as AWS S3 or Lambda! Infracost has many CI/CD integrations so you can easily post cost estimates in pull requests. This provides your team with a safety net as people can discuss costs as part of the workflow. -
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DiffEqBayes.jl
Extension functionality which uses Stan.jl, DynamicHMC.jl
This repository is a set of extension functionality for estimating the parameters of differential equations using Bayesian methods. It allows the choice of using CmdStan.jl, Turing.jl, DynamicHMC.jl and ApproxBayes.jl to perform a Bayesian estimation of a differential equation problem specified via the DifferentialEquations.jl interface. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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DataDrivenDiffEq.jl
Data driven modeling and automated discovery of dynamical systems
DataDrivenDiffEq.jl is a package for finding systems of equations automatically from a dataset. The methods in this package take in data and return the model which generated the data. A known model is not required as input. These methods can estimate equation-free and equation-based models for discrete, continuous differential equations or direct mappings. -
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DePay
JavaScript library to integrate various web3 crypto wallets
DePay simplifies and improves Web3 Payments with the power of DeFi. Accept any token with on-the-fly conversion. You want to accept and send payments worldwide, peer-to-peer in a decentralized, censorship- and fraud-resistant way? We are providing innovative, blockchain-based solutions to do so. It's easy to integrate and easy to use. Perform and manage Web3 Payments today. Everything starts with a simple integration. We worked hard so that you don't have to spend months implementing Web3... -
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DSGE.jl
Solve and estimate Dynamic Stochastic General Equilibrium models
DSGE.jl is a Julia package developed by the Federal Reserve Bank of New York for estimating and analyzing dynamic stochastic general equilibrium (DSGE) models. It provides tools for Bayesian estimation, filtering, forecasting, and model comparison, supporting both academic research and policy applications. DSGE.jl includes pre-configured models used by central banks and offers extensibility for custom macroeconomic modeling. -
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Criterium
Benchmarking library for clojure
...Inclusion of a warm-up period, designed to allow the JIT compiler to optimise its code. Purging of gc before testing, to isolate timings from GC state prior to testing. A final forced GC after testing to estimate impact of cleanup on the timing results. -
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Causal ML
Uplift modeling and causal inference with machine learning algorithms
Causal ML is a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research [1]. It provides a standard interface that allows users to estimate the Conditional Average Treatment Effect (CATE) or Individual Treatment Effect (ITE) from experimental or observational data. Essentially, it estimates the causal impact of intervention T on outcome Y for users with observed features X, without strong assumptions on the model form. An important lever to increase ROI in an advertising campaign is to target the ad to the set of customers who will have a favorable response in a given KPI such as engagement or sales. ... -
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SAM 3D Body
Code for running inference with the SAM 3D Body Model 3DB
SAM 3D Body is a promptable model for single-image full-body 3D human mesh recovery, designed to estimate detailed human pose and shape from just one RGB image. It reconstructs the full body, including feet and hands, using the Momentum Human Rig (MHR), a parametric mesh representation that decouples skeletal structure from surface shape for more accurate and interpretable results. The model is trained to be robust in diverse, in-the-wild conditions, so it handles varied clothing, viewpoints, and backgrounds while maintaining strong accuracy across multiple human-pose benchmarks. ... -
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Denoising Diffusion Probabilistic Model
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to generative modeling that may have the potential to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution. If you simply want to pass in a folder name and the desired image dimensions, you can use the Trainer class to easily train a model. -
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NBA Sports Betting Machine Learning
NBA sports betting using machine learning
...Using this dataset, the project constructs matchup features that represent team performance trends and contextual information about each game. Machine learning models are then trained to estimate the probability that a team will win a game as well as whether the total score will fall above or below the sportsbook’s predicted total. In addition to predicting outcomes, the project evaluates expected value to determine whether a potential bet offers a statistical advantage compared with sportsbook odds. -
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Basketball Reference
NBA Stats API via Basketball Reference
...I initially wrote this library as an exercise for creating my first PyPi package, hope you find it valuable! This library was created for another Python project where I was trying to estimate an NBA player's productivity. A lot of sports-related APIs are expensive - luckily, Basketball Reference provides a free service which can be scraped and translated into a usable API. -
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Faast.js
Serverless batch computing made simple
...Zero-configuration support for bundling dependencies with built-in webpack support. Use native dependencies, even on AWS Lambda. Faast.js eliminates the friction of using packages with serverless functions. Cost snapshots estimate the cost of your serverless function invocations in real time. For deeper analysis, use the Cost Analyzer to estimate the cost of workloads against multiple serverless configurations. -
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Cleanlab
The standard data-centric AI package for data quality and ML
cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset. To facilitate machine learning with messy, real-world data, this data-centric AI package uses your existing models to estimate dataset problems that can be fixed to train even better models. cleanlab cleans your data's labels via state-of-the-art confident learning algorithms, published in this paper and blog. See some of the datasets cleaned with cleanlab at labelerrors.com. This package helps you find label issues and other data issues, so you can train reliable ML models. ... -
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Cloud Cost Handbook
Set of guides meant to help explain often-times complex pricing
...It’s intended as an educational resource for developers, finance teams, and FinOps practitioners who need to understand how cloud costs are structured, what drives pricing across services, and how to estimate, optimize, and forecast spend without wading through dense official documentation. The handbook covers core concepts, pricing examples, cost-optimization techniques, and billing models across providers such as AWS, Azure, and GCP, with each section curated to improve clarity and accessibility for readers without deep financial or cloud billing expertise. ... -
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Gollama
Go manage your Ollama models
...Beyond standard model management, Gollama can display metadata such as size, quantization level, model family, and modification date, which helps users compare models quickly. One of its more distinctive capabilities is a VRAM estimation system that can calculate memory requirements, estimate context limits, and help users choose quantization settings that fit available hardware. -
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TokenCost
Easy token price estimates for 400+ LLMs. TokenOps
TokenCost is an open-source developer utility designed to estimate the cost of using large language model APIs by calculating token usage and translating it into real monetary values. The tool focuses on helping developers understand how much their prompts and generated completions cost when interacting with commercial AI models. It works by counting tokens in prompts and responses before or after sending requests and then applying pricing information associated with different models. ... -
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SteadyDancer
Harmonized and Coherent Human Image Animation
SteadyDancer is a research-oriented motion stabilization and dancer tracking system designed to analyze and correct motion in videos, making captured performances appear smoother and more stable while preserving expressiveness. It employs computer vision and motion modeling to estimate and reduce unwanted jitters, shakes, or camera wobbles — particularly in dance or movement sequences where traditional smoothing would distort intentional motion. By differentiating between intentional rhythmic motion and unintentional instability, SteadyDancer applies adaptive filtering that enhances video quality without flattening the core movement dynamics. ... -
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CausalImpact
An R package for causal inference in time series
The CausalImpact repository houses an R package that implements causal inference in time series using Bayesian structural time series models. Its goal is to estimate the effect of an intervention (e.g. a marketing campaign, policy change) on a time series outcome by predicting what would have happened in a counterfactual “no intervention” world. The package requires as input a response time series plus one or more control (covariate) time series that are assumed unaffected by the intervention, and it divides the time horizon into “pre-intervention” and “post-intervention” periods. ...
