9,461 results

Tests M random samples of N random vars to determine if they are from Inverse Gaussian distbtn.

This program is based on the method published by O'Reilly & Rueda (see program for more details for reference). Confidence level (i.e. alpha value) is chosen,and if the number of AD test stats

Anderson-Darling k-sample procedure to test whether k sampled populations are identical.

Anderson and Darling (1952, 1954) introduced a goodness-of-fit statistic to test the hypothesis that a random sample comes from a continuous population with a specified distribution function. It is a

Anderson-Darling test for assessing normality of a sample data.

The Anderson-Darling test (Anderson and Darling, 1952) is used to test if a sample of data comes from a specific distribution. It is a modification of the Kolmogorov-Smirnov (K-S) test and gives more

Anderson-Darling test for assessing Weibull distribution of a sample data.

The Anderson-Darling test (Anderson and Darling, 1952) is used to test if a sample of data comes from a specific distribution. It is a modification of the Kolmogorov-Smirnov (K-S) test and gives more

ModifiedADforGumbel calculates Modified Anderson-Darling test statistic and estimates p-value

ModifiedADforGumbel calculated Modified Anderson-Darling test statistic and estimates p-value for Extreme Value distribution (GEV tYpe I) for maxima. The EV parameters are eslimated by the Method of

Anderson-Darling test for assessing exponential distribution of a sample data.

The Anderson-Darling test (Anderson and Darling, 1952) is used to test if a sample of data comes from a specific distribution. It is a modification of the Kolmogorov-Smirnov (K-S) test and gives more

3 goodnes of fit tests for thw Weibull: Anderson-Darling, Chi-squared and graphic

3 goodnes of fit tests for thw Weibull: Anderson-Darling, Chi-squared and graphic

Shapiro-Wilk & Shapiro-Francia parametric hypothesis test of composite normality.

Shapiro-Wilk parametric hypothesis test of composite normality, for sample size 3<= n <= 5000. Based on Royston R94 algorithm.This test also performs the Shapiro-Francia normality test for

This function calculates p-values for ten well-known normality tests.

)), Anderson-Darling (AD) test, Cramer-Von Mises (CvM) test, Shapiro-Wilk (SW) test, Shapiro-Francia (SF) test, Jarque-Bera (JB) test, D’Agostino and Pearson (DAP) test. Tests are not meant for big data. Most tests does

Anderson acceleration of the alternating projections method for the nearest correlation matrix.

MATLAB codes for Anderson acceleration for the alternating projectionsmethod for the nearest correlation matrix problem, based on the paperNicholas J. Higham and Nataša Strabić. Anderson

Exploring techniques for estimating the safety of machine learning classifiers

detection of simulated network traffic, using distributional shift detection measures including the Kolmogorov-Smirnov, Kuiper, Anderson-Darling, Wasserstein and mixed Wasserstein-Anderson-Darling measures

A set of functions for well-known Empirical cumulative distribution function (ECDF)-based distance measures.

DistanceAnderson-Darling DistanceKolmogorov Smirnov DistanceCramer von Mises DistanceKuiper DistanceWasserstein-Anderson-Darling DistanceRelated WorksThe code has been converted to MATLAB from "twosamples" library of R

This example project can be used as a reference design to get started with designing Battery Management System with MATLAB and Simulink.

Management - Over/Under Voltage, Over Current, Over Temperature etc. 5. Charge and Discharge Current Limit Calculations To design and test these algorithms, project also includes files for 1. Li-ion Battery

Live Script exploring tests of an empirical distribution for normality.

Anderson-Darling test and a Kolmogorov-Smirnov test for normality.This script may interest students and educators in physics and other STEM fields. Sliders are provided to adjust the empirical distribution

Verify and test robustness of deep learning networks, deploy with confidence

AI Verification Library for Deep Learning Toolbox allows you to verify and test properties of deep learning networks, and deploy these models with confidence.Use this library to:Verify network

Prototype and test software-defined radio (SDR) systems using ADALM-PLUTO with MATLAB and Simulink

use ADALM-Pluto Radio as a standalone peripheral for live RF data I/O using MATLAB functions or Simulink blocks. This lets you quickly test your transmitter and receiver designs under real-world

A fast test to determine point inclusion for general polygonal geometries.

examplepolydemo(2); % multiply-connected domainspolydemo(3); % speed comparisonINPOLY implements a sorted 'crossing-number' test designed to achieve fast performance for complex inputs. Given a configuration with N

Test wideband wireless systems and perform spectrum monitoring

spectrum monitoring and the testing of wideband wireless systems. Additionally, high-rate signal capture enables data set creation for the training of deep-learning models for wireless applications.You can

Calculate the Student t Test for unequal or equal samples size, unpaired or paired samples.

Student's t test for unpaired or paired samples.This file is applicable for equal or unequal sample sizes; for paired or unpaired samples. When the test is unpaired, the Fisher-Snedecor F-test

Estimates p-value of acceptance of a simple H0: "CDF with parameters estimated of given sample"

puts the same weight on an entire range of X (unlike the Anderson-Darling test putting more weight on tails of a distribution).

MATLAB function to create a zone plate test image

and also for testing the anti-aliasing capability of image resizing methods.For more

This is a presentation on testing of safety critical control systems.

This has a presentation on testing Safety Critical Control Systems and a brief introduction to Formal Methods

A nonparametric Test for Trend

Perform the Jonckheere-Terpstra test on trend.There are situations in which treatments are ordered in some way, for example the increasing dosages of a drug. In these cases a test with the more

A Wilcoxon-Type Test for Trend

Perform the Cuzick's test on trend.This function provides a Wilcoxon-type test for trend across a group of three or more independent random samples. Assumptions: Data must be at least ordinal

This function automatically validates the normality of your data using 9 appropriate statistical and visualization techniques.

your data against renowned statistical tests including Shapiro-Wilk, Kolmogorov-Smirnov, Anderson-Darling, Jarque-Bera, and Lilliefors tests. The function compiles resulting P-values in a concise table

Test whether addition of model parameters is warranted by improvement of data misfits.

This function uses an F-test to determine the likelihood that an observed improvement of a fit to data warrants the use of additional parameters.Usage:>> [ p, Fstat, df1, df2 ] =

Automatically generate seed scenarios for the Euro NCAP test protocols and generate scenario variants.

The Automated Driving Toolbox™ Test Suite for Euro NCAP^® Protocols support package enables you to automatically generate specifications for various Euro NCAP^® tests, which include safety assessments

Randomness measure of 'number of pixel changing rate' and 'unified average changed intensity'

This single m file implements the randomness measure of NPCR and UACI, and produces the classic quantitative scores and the new qualitative randomness test p-Vals.

This support package adds ASAM XIL Standard support to the Test Manager in Simulink Test.

The Simulink^® Test™ Support Package for ASAM^® XIL Standard implements the ASAM XIL API, which is a standard that defines communication between test automation tools, such as Simulink Test, and test

Perform the McNemar test on a 2x2 matrix

Permorm McNemar's chi square on a 2x2 matrixIn statistics, McNemar's test is a non-parametric method used on nominaldata to determine whether the row and column marginal frequencies areequal. It is

A permutation test (aka randomization test) for MATLAB, testing for a difference in means between two samples.

A permutation test (aka randomization test) for MATLAB, testing for a difference in means between two samples. It supports one- and two-tailed tests, and returns a p-value, the observed difference

Implements the Gottwald - Melbourne 0 - 1 test for chaos

In 2004 Georg Gottwald and Ian Melbourne introduced a new test for chaos (Proc. Roy. Soc. A 460, 603–611). The input is any time series, that may come from a discrete map, a differential equation or

Test two key cognitive skills, response inhibition and selective attention

Because most people’s automatic response is to read a word, the Stroop Test is a classic test of response inhibition. This skill involves responding quickly while avoiding incorrect impulses that may

Build automatically the IEEE 123 Node Test Feeder and compare simulation to benchmark results

This submission contains MATLAB scripts that will build automatically the IEEE 123 Node Test Feeder in Simscape Electrical - Specialized Power Systems. The model is a 'quasi-steady' model, meaning it

Small signal stability

Mann-Whitney-Wilcoxon non parametric test for two unpaired groups.

This file execute the non parametric Mann-Whitney-Wilcoxon test to evaluate thedifference between unpaired samples. If the number of combinations is less than20000, the algorithm calculates the exact

The test calculate the performance of a clinical test based on the Bayes theorem

) - Test Accuracy - Mis-classification Rate - F-Measure - Test bias - Error odds ratio - Diagnostic odds ratio - Discriminant Power Example: x=[731 270;78 1500

Perform permutation test to estimate P-value for one-way ANOVA

Follows Anderson (doi:10.1139/cjfas-58-3-626) to estimate P-values for one-way ANOVA using permutation of data over grouping variable.

Seasonal trends using tau-a,b and seasonal slope with intervals, accounts for serial dependence.

Seasonal Kendall Trend Test for Data with and without Searial Dependance.Tau-b seasonal: takes into account ties (and multiple observations, except, the data are preprocessed in a subfucntion and

Dunn's procedure for multiple, non parametric, comparisons

Dunn's test is the not parametric analog of Holm-Sidak multiple t-test. When you use the Kruskal-Wallis test, you know if there is a difference among your groups, but you can't apply the KWtest for

Tire test data is processed and Magic Formula coefficients are calculated.

TireDataAnalysis.mlx processes tire test data from acceleration and brake as well as cornering tests and returns the Pacejka’s Magic Formula coefficients for both a longitudinal and lateral model.

generates random variates from over 870 univariate distributions

Numerical solution of a Prandtl-Meyer expansion wave flow field based on Anderson's CFD book

applications" from J.D.Anderson. As a result, this code might be useful for those trying to solve the problem, but also for anyone who is interested in supersonic aerodynamics.

Matrix Controllability Test

Matrix Controllability Test

This document includes several statistical tests to identify outliers in data series.

identify suspicious observations that would require further analysis and also tests to determine if some observations are outliers. Nevertheless, it would be dangerous to blindly accept the result of a test

In this model, a Microgrid test system based on the 14-busbar IEEE distribution system is proposed.

Hz.This test system simulation includes:• One diesel generator,• Two photovoltaic (PV) systems,• Two battery energy storage system,• Various linear and non-linear loads.Additionally, the DC microgrid model

The chi-squared test.

Usage: [p, Q]= chi2test(x) The chi-squared test. Given a number of samples this function tests the hypothesis that the samples are independent. If Q > chi2(p, nu), the hypothesis is rejected

Build automatically the IEEE 906 Bus European LV Test Feeder and compare to benchmark results

This submission contains MATLAB scripts that will build automatically the IEEE 906 Bus European LV Test Feeder in Simscape Electrical - Specialized Power Systems. The model is a 'quasi-steady' model

Source Panel Method applied to Flow around Cylinder. Example 3.19 from Anderson's book

Source Panel Method applied to Flow around Cylinder. Example 3.19 from Anderson's "Fundamentals of Aerodynamics". Serves as a good primer to CFD.

This is simple utility for Raspberry Pi board LED testing

Comparing survival curves of two groups using the log rank test

Comparing survival curves of two groups using the log rank test.Comparison of two survival curves can be done using a statistical hypothesis test called the log rank test. It is used to test the null

Mann-Kendall non-parametric trend test.

The code performs original two tailed Mann-Kendall test. It tests the null hypothesis of trend absence in the vector V, against the alternative of trend. The result of the test is returned in H = 1

Cramer-von Mises test for goodness-of-fit of a single sample

Cramer-von Mises test for goodness-of-fit of a single sample based on the exact and asymptotic distribution of Csörgo & Faraway (1996).

Test for differences between two multidimensional distributions (2-d K-S test, n-d energy test)

Functions for non-parametrically testing whether two multidimensional samples were drawn from the same parent distribution. One function implements Fasano & Franceschini's generalization [1] to

This function performs the chi-square test for 2x2 contingency tables

square test which is based on the chi square distribution. Thechi square test might become unreliable when the total number of expected frequencies(cell values in the contingency table) are not large

Cochran's Q Test on dichotomous data for k-related samples

H = COCHRANQTEST(X) performs the non-parametric Cochran's Q-test on the hypothesis that the K columns of N-by-K matrix have the same number of successes and failures. H==0 indicates that the null

A test for chaotic dynamics of a noisy time series based on the Lyapunov exponent.

This test performs the test for chaotic dynamics of a noisy time series based on the Lyapunov exponent. The input is a vector of observed time series which can be stochastic or chaotic, usually time

The stationarity of a time series is evaluated by using the reverse arrangement test or moving-window functions

stationaryTestsMatlab functions to test the stationarity of a random processSummaryThe N-th order stationarity [1] of a random process is assessed using two tests. In the present submission, only the

A modified version of the Mann-Kendall Test that works with autocorrelated data.

Modified-MannKendall-TestReleased on MATLAB FileExchangeTo get started refer the file demo.mUseThe modified Mann-Kendall test is a hypothesis test that determines whether a given sequence of data has

Alphanumeric sort of filenames or filepaths, with customizable number format.

NATSORTFILES.Examples>> A = {'a2.txt', 'a10.txt', 'a1.txt'}>> sort(A)ans = 'a1.txt' 'a10.txt' 'a2.txt'>> natsortfiles(A)ans = 'a1.txt' 'a2.txt' 'a10.txt'>> B = {'test2.m'; 'test10-old.m'; 'test