Get the average value in an array
Statistical routines and probability distributions.
Takes a Feature or FeatureCollection and returns the mean center.
Calculates the average angle of a set of lines, measuring the trend of it.
Generate TypeScript types and interfaces using JavaScript or TypeScript code
A streaming parser for HTML form data for node.js
A node API for the dprint TypeScript and JavaScript code formatter
Yet another javascript fuzzy matching library
Chi-squared distribution expected value.
Takes a FeatureCollection of points and calculates the median center.
Fuzzy match a command from a list (typo-safety)
A stylish, editor-like, modular, react component for viewing, editing, and debugging javascript object syntax!
"did you mean" for oclif
Calculate the arithmetic mean of a strided array.
Beta distribution expected value.
RUM Distiller is a JavaScript library for data exploration of Adobe RUM data. It allows you to define the shape of the data first, in the form of "series", "groups", and "facets". You can then filter the data based on the defined facets, and will automati
Perform the Student's t hypothesis test
Calculate the arithmetic mean of a strided array.
[](https://www.npmjs.com/package/@camunda8/sdk)
Generate a signature for Apollo usage reporting
Gamma distribution expected value.
Define a data property on an object. Will fall back to assignment in an engine without descriptors.
Calculates an index based the average distances between points in the dataset, thereby providing inference as to whether the data is clustered, dispersed, or randomly distributed within the study area.
A JavaScript model of a Gaussian distribution
Display a single or multiple progress bars in the terminal. A progress bar can show determinate or indeterminate progress that can be paused and resumed at any time. A bar format supports many tokens for common information display like elapsed time, estimated time to completion, mean rate and more.
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.
A suite for basic and advanced statistics on Ruby. Tested on CRuby 1.9.3, 2.0.0 and 2.1.1. See `.travis.yml` for more information. Include: - Descriptive statistics: frequencies, median, mean, standard error, skew, kurtosis (and many others). - Correlations: Pearson's r, Spearman's rank correlation (rho), point biserial, tau a, tau b and gamma. Tetrachoric and Polychoric correlation provides by statsample-bivariate-extension gem. - Intra-class correlation - Anova: generic and vector-based One-way ANOVA and Two-way ANOVA, with contrasts for One-way ANOVA. - Tests: F, T, Levene, U-Mannwhitney. - Regression: Simple, Multiple (OLS), Probit and Logit - Factorial Analysis: Extraction (PCA and Principal Axis), Rotation (Varimax, Equimax, Quartimax) and Parallel Analysis and Velicer's MAP test, for estimation of number of factors. - Reliability analysis for simple scale and a DSL to easily analyze multiple scales using factor analysis and correlations, if you want it. - Dominance Analysis, with multivariate dependent and bootstrap (Azen & Budescu) - Sample calculation related formulas - Structural Equation Modeling (SEM), using R libraries +sem+ and +OpenMx+ - Creates reports on text, html and rtf, using ReportBuilder gem - Graphics: Histogram, Boxplot and Scatterplot.