Search Results for "compute"

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of...

A set of Matlab functions which compute effect size statistics and (exact) confidence intervals for a wide range of data analysis situations, including two-sample-, oneway-, twoway- and contrast analyses as well as categorical data in tables.

IterInt is a numerical integration package that implements iterated numerical integration methods. Iterated methods can be used to compute low dimensional (less than ten, for example) integration problems to get very accurate results.

The Robust Correlation Toolbox is a free collection of Matlab functions allowing to visualize data in univariate and bivariate space, check assumptions of normality and homoscedasticity and compute Pearson's and Spearman's, percentage bend, and skipped correlations with bootstrapped confidence intervals - see http://www.frontiersin.org/Quantitative_Psychology_and_Measurement/10.3389/fpsyg.2012.00606/full -- code moved to https://github.com/CPernet/robustcorrtool

FastPval is multiple stage p-value computing software that computes empirical p-values from a large set of permutated/resampled background data.

A MATLAB package to simulate sample paths of the solution of a Itô or Stratonovich stochastic differential equation (SDE), compute statistics and estimate the parameters from data. A note of caution: SDE Toolbox is no more developed but it's still downloadable. Its inferential capabilities can be considered surpassed (at best). Actually the parameter estimation methods were already far from the state-of-art when the project began in 2007 (!). The considered implemented parametric and non-parametric Monte Carlo likelihood methods were chosen for their ability to treat both one-dimensional and multivariate SDE systems, although the quality of the inferential results can't match those obtained using more advanced techniques. ...