> Time-Weighted Average Price
Number averaging util
calculate weighted average to get a rating
Recursively calculate the weighted average (WA) of a array of objects
Multi-input adder with optional weighting - sum or weighted average of inputs
Octo ERP module — inventory management with weighted average costing
Various weighted average functions
Pure javascript library for high quality image transforms like affine, perspective, arc using algorithms from ImageMagick and incorporates reverse pixel mapping technique and elliptical weighted average resampling.
Calculate the Volume-Weighted Average Price (VWAP)
For the GB power market, compare weighted average CFD prices to the (gas driven) Market Index Data Price
Aggregates cryptocurrency prices from various exchanges. Prices from different exchanges are weighted by their volume. The result is the Volume-Weighted-Average-Market-Pair-Price (VWAMPP) for each cryptocurrency.
Quickly calculate moving volume-weighted average prices [aka VWAP or MVWAP] from a stream of OHLCV candles. Uses prefix sums and static 'rolling' arrays for efficiency.
> Time-Weighted Average Price
Do you need location weighted average? You can use my library.
The FinTech utility collections of simple, cumulative, and exponential moving averages.
Weighted average cost of capital (WACC) calculator of listed and unlisted companies based on an input-based API calculation engine.
> Time-Weighted Average Price
D3 plugin which computes a Weighted Voronoi tesselation
Exponentially Weighted Moving Average
Collection of ~170 lightweight, composable transducers, reducers, generators, iterators for functional data transformations
A simple library that calculates the average color of images, videos and canvas in browser environment.
Production-ready finance library for portfolio construction, risk analytics, quantitative metrics, and ML-based regime detection
<div align="center"> <img width="200" height="200" src="https://s3.amazonaws.com/pix.iemoji.com/images/emoji/apple/ios-11/256/crayon.png"> <h1>@jimp/plugin-mask</h1> <p>mask an image with another image.</p> </div>
Highly customizable stock charts with ReactJS and d3
Comprehensive evaluation metrics for ToRSh - powered by SciRS2
Pure-Rust Fuzzy Inference System (Mamdani + TSK) with PSO optimizer — zero external dependencies
Core analysis engine for oxide-sloc — file discovery, SLOC counting, and coverage parsing
Perform weighted averages, even across associations. Rails 3 only because it uses ARel.
A simple gem/dsl for generating Weighted Average Score calculations.
Basic statistics including mean, median, weighted moving average and growth.
A ratings system for Rails apps using MongoDB, with bayesian and straight averages, and weighting.
Noisy sensor data, approximations in the equations that describe the system evolution, and external factors that are not accounted for all place limits on how well it is possible to determine the system's state. The Kalman filter deals effectively with the uncertainty due to noisy sensor data and to some extent also with random external factors. The Kalman filter produces an estimate of the state of the system as an average of the system's predicted state and of the new measurement using a weighted average. The purpose of the weights is that values with better (i.e., smaller) estimated uncertainty are "trusted" more. The weights are calculated from the covariance, a measure of the estimated uncertainty of the prediction of the system's state. The result of the weighted average is a new state estimate that lies between the predicted and measured state, and has a better estimated uncertainty than either alone. This process is repeated at every time step, with the new estimate and its covariance informing the prediction used in the following iteration. This means that the Kalman filter works recursively and requires only the last "best guess", rather than the entire history, of a system's state to calculate a new state.
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