Search Results for "estimate"
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Showing 296 open source projects for "estimate"
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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. -
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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. -
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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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gitoxide
An idiomatic, lean, fast & safe pure Rust implementation of Git
An idiomatic, lean, fast & safe pure Rust implementation of Git. gix is a command-line interface (CLI) to access git repositories. It's written to optimize the user experience and perform as well or better than the canonical implementation. Furthermore, it provides an easy and safe to use API in the form of various small crates for implementing your own tools in a breeze. Please see 'Development Status' for a listing of all crates and their capabilities. Please note that all functionality... -
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Parabol
Free online agile retrospective meeting tool
...Add anonymous suggestions, create tasks as you go, and document your conversation in real-time with discussion threads – with your remote or hybrid team. Bring Planning Poker to life remotely. Sync issues from your backlog management tool and estimate with a fun digital card deck. Save your team time while staying up-to-date. Connect with team-mates – no matter where you all are, share updates on your own time, review updates when you’re ready. Celebrate your progress without note-taking or duplicating work, connect with your team with [optional] icebreakers, and create takeaway tasks together. ... -
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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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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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3D Gaussian Splatting
Original reference implementation of "3D Gaussian Splatting"
...The project is widely used in computer graphics, spatial capture, virtual production, research, and experimental 3D reconstruction workflows. It relies on image-based reconstruction pipelines such as COLMAP to estimate camera positions before optimizing the Gaussian representation. Overall, Gaussian Splatting has become a foundational reference implementation for modern real-time radiance field rendering. -
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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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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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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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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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Sloc Cloc and Code (scc)
Sloc, Cloc and Code: scc is a very fast accurate code counter
...For counting the lines of code, blank lines, comment lines, and physical lines of source code in many programming languages. The goal is to be the fastest code counter possible, but also perform COCOMO calculations like sloccount, estimate code complexity similar to cyclomatic complexity calculators, and produce unique lines of code or DRYness metrics. In short one tool to rule them all. -
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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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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. ...
