In probability theory and statistics, covariance is a measure of how much two random variables change together. http://en.wikipedia.org/wiki/Covariance
Computes the covariance between one or more numeric arrays.
Calculate covariance
Online algorithm for calculating covariance
Compute an unbiased sample covariance incrementally.
Calculate covariance
online average, variance, covariance and correlation
Compute an unbiased sample covariance matrix incrementally.
Compute a moving unbiased sample covariance incrementally.
Calculate the covariance of two strided arrays provided known means and using a one-pass textbook algorithm.
Calculate the covariance of two double-precision floating-point strided arrays provided known means and using a one-pass textbook algorithm.
Reduce transform stream which calculates the covariance of streamed numeric data.
Calculate aggregation tests and meta-analysis of aggregation test results using score statistics and covariance matrices
Calculate the covariance of two single-precision floating-point strided arrays provided known means and using a one-pass textbook algorithm.
Compute the covariance of two one-dimensional ndarrays provided known means and using a one-pass textbook algorithm.
Compute the covariance of two one-dimensional single-precision floating-point ndarrays provided known means and using a one-pass textbook algorithm.
Compute the covariance of two one-dimensional double-precision floating-point ndarrays provided known means and using a one-pass textbook algorithm.
generate random covariance matrices, and MVN samples using them.
An average, variance, covariance and correlation calculator written in TypeScript
Compute the covariance matrix for an `M` by `N` double-precision floating-point matrix `A` and assigns the results to a matrix `B` when provided known means and using a one-pass textbook algorithm.
average, standard deviation, variance, covariance and correlation of data sets
Fast and Numerically Stable Statistical Analysis Utilities
An open source scientific computing library for JS and TS developers
Compute a sample Pearson product-moment correlation coefficient.
Covariance matrix denoising for financial risk validation. Marchenko-Pastur filtering, Ledoit-Wolf shrinkage, detoning. Pure math, no I/O.
Yahoo Finance price + ISIN provider for the regit-covariance pipeline.
A lending-iterator trait based on higher-rank trait bounds, with full std::iter::Iterator functionality
Covariance estimation algorithms
Lean-backed fixed-dimension streaming covariance and Ledoit-Wolf shrinkage
Pure Rust implementation of Minuit-style parameter optimization
Covariance estimation for the ferrolearn ML framework
Dead simple implementation of Discrete Kalman filter for object tracking purposes
A high-performance survival analysis library written in Rust with Python bindings
Septentrio Binary Format (SBF) parser library
EEG source localization (MNE / dSPM / sLORETA / eLORETA) — pure Rust
Orbit determination toolkit in Rust. Provides astrometric parsing, observer management, and initial orbit determination (Gauss method) with JPL ephemeris support.
This gem allows to calculate the correlation of two variables (called vectors), the standard deviation and the covariance.
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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