Matlab functions for maximum likelihood estimation of a variety of probabilistic discounting models from choice experiments. Data should take the form of binary choices between immediate and delayed rewards. The available discount functions are: 1) exponential 2) hyperbolic (Mazur's one-parameter hyperbolic) 3) generalized hyperbolic 4) Laibson's beta-delta

Compositional data, which is data consisting of fractions or probabilities, is common in many fields including ecology, economics, physical science and political science. If these data would otherwise be normally distributed, their spread can be conveniently represented by a multivariate normal distribution truncated to the non-negative space under a unit simplex. Here this distribution is called the simplex-truncated multivariate normal distribution. For calculations on truncated distributions, it is often useful to obtain rapid estimates of their integral, mean and covariance; these quantities characterising the truncated distribution will generally possess different values to the corresponding non-truncated distribution.

In the paper Adams, Matthew (2022) Integral, mean and covariance of the simplex-truncated multivariate normal distribution. PLoS One, 17(7), Article number: e0272014. https://eprints.qut.edu.au/233964/, three different approaches that can estimate the integral, mean and covariance of any simplex-truncated multivariate normal distribution are described and compared. These three approaches are (1) naive rejection sampling, (2) a method described by Gessner et al. that unifies subset simulation and the Holmes-Diaconis-Ross algorithm with an analytical version of elliptical slice sampling, and (3) a semi-analytical method that expresses the integral, mean and covariance in terms of integrals of hyperrectangularly-truncated multivariate normal distributions, the latter of which are readily computed in modern mathematical and statistical packages. Strong agreement is demonstrated between all three approaches, but the most computationally efficient approach depends strongly both on implementation details and the dimension of the simplex-truncated multivariate normal distribution.

This dataset consists of all code and results for the associated article.

Abstract: This contribution provides MATLAB scripts to assist users in factor analysis, constrained least squares regression, and total inversion techniques. These scripts respond to the increased availability of large datasets generated by modern instrumentation, for example, the SedDB database. The download (.zip) includes one descriptive paper (.pdf) and one file of the scripts and example output (.doc). Other Description: Pisias, N. G., R. W. Murray, and R. P. Scudder (2013), Multivariate statistical analysis and partitioning of sedimentary geochemical data sets: General principles and specific MATLAB scripts, Geochem. Geophys. Geosyst., 14, 4015–4020, doi:10.1002/ggge.20247.

File List meta_fact.zip -- zip file containing the following eight MATLAB function files: fact_hedges_d.m -- A Matlab function that returns the individual, overall, and interaction effect sizes for 2 "agents" in a 2 × 2 factorial experiment, where effect size is measured using Hedges' d; the sampling variances of each effect size are also returned. fact_logRR.m -- A Matlab function that returns the individual, overall, and interaction effect sizes for 2 "agents" in a 2 × 2 factorial experiment, where effect size is measured using the log response ratio; the sampling variances and degrees of freedom of each effect size are also returned. J.m -- A Matlab function that computes the small-sample size correction factor J. Q.m -- A Matlab function that computes a weighted sum of squares. mean_effect.m -- A Matlab function that returns a weighted mean effect size and its 95% confidence limits, where the weights include the among-study variance if it is significant at P < 0.05. Best used when effect sizes are measured using Hedges' d; for the log response ratio, use mean_effect_L. mean_effect_L.m -- A Matlab function that returns the weighted mean log response ratio effect size, its SE, and its 95% confidence limits, where the weights include the among-study variance, the significance of which (from a chi-square test on the sum of squares) is returned as well. test_Qb_mixed_2.m -- A Matlab function that tests for a significant between-class sum of squares in a mixed-model meta-analysis comparing two classes. test_Qb_mixed_n.m -- A Matlab function that tests for a significant between-class sum of squares in a mixed-model meta-analysis comparing n classes. Description This supplement includes Matlab code to compute individual, overall, and interactive effects using Hedges’ d and the log response ratio, to calculate weighted mean effect sizes, and to perform mixed-model homogeneity tests. Functions mean_effect, mean_effect_L, test_Qb_mixed_2, and test_Qb_mixed_n all use the function chi2cdf from the Matlab Statistics Toolbox. Additional documentation appears as comments at the beginning of each function file; once the files have been downloaded into a folder in the Matlab path, typing help function_name (e.g., help fact_logRR) at the Matlab command prompt will display the descriptive comments.

This zip file contains the data and the Matlab scripts for data analysis and generation of figures.

This is a zip file containing the example data for nSTAT matlab toolbox (http://doi.org/10.1016/j.jneumeth.2012.08.009) . The data directory should be un-zipped into the main nSTAT directory. Updated 7-2-2017

MATLAB led the global advanced analytics and data science software industry in 2025 with a market share of ***** percent. First launched in 1984, MATLAB is developed by the U.S. firm MathWorks.

Comprehensive YouTube channel statistics for MATLAB TECH, featuring 210,000 subscribers and 7,962,014 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Education category and is based in US. Track 557 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

This is sample Matlab script for postprocessing of DHSVM bias and low flow corrected data using Integrated Scenarios Project CMIP5 climate forcing data to model future projected streamflow in the Skagit River Basin. Testing HydroShare Collections...testing, testing.

Network datasets and meta data using in work which develops a new model of spatial network structure: http://arxiv.org/abs/1210.4246

Original source here.

This dataset was collected in the Cranfield Multiphase Flow Facility aiming to serve as a benchmark case for statistic process monitoring.

Read this paper for more information.

Cite as:

Yi Cao (2020). A Benchmark Case for Statistical Process Monitoring - Cranfield Multiphase Flow Facility (https://www.mathworks.com/matlabcentral/fileexchange/50938-a-benchmark-case-for-statistical-process-monitoring-cranfield-multiphase-flow-facility), MATLAB Central File Exchange. Retrieved November 23, 2020.

License:

Copyright (c) 2015, Yi Cao All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

• Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

• Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Abstract: We present here annotated MATLAB scripts (and specific guidelines for their use) for Q-mode factor analysis, a constrained least squares multiple linear regression technique, and a total inversion protocol, that are based on the well-known approaches taken by Dymond (1981), Leinen and Pisias (1984), Kyte et al. (1993), and their predecessors. Although these techniques have been used by investigators for the past decades, their application has been neither consistent nor transparent, as their code has remained in-house or in formats not commonly used by many of today's researchers (e.g., FORTRAN). In addition to providing the annotated scripts and instructions for use, we include a sample data set for the user to test their own manipulation of the scripts. Other Description: Pisias, N. G., R. W. Murray, and R. P. Scudder (2013), Multivariate statistical analysis and partitioning of sedimentary geochemical data sets: General principles and specific MATLAB scripts, Geochem. Geophys. Geosyst., 14, 4015–4020, doi:10.1002/ggge.20247.

We contrast two theoretical positions on the relation between phenomenal and access consciousness. First, we discuss previous data supporting a mild Overflow position, according to which transient visual awareness can overflow report. These data are open to two interpretations: (i) observers transiently experience specific visual elements outside attentional focus without encoding them into working memory; (ii) no specific visual elements but only statistical summaries are experienced in such conditions. We present new data showing that under data-limited conditions observers cannot discriminate a simple relation (same versus different) without discriminating the elements themselves and, based on additional computational considerations, we argue that this supports the first interpretation: summary statistics (same/different) are grounded on the transient experience of elements. Second, we examine recent data from a variant of ‘inattention blindness’ and argue that contrary to widespread assumptions, it provides further support for Overflow by highlighting another factor, task relevance, which affects the ability to conceptualize and report (but not experience) visual elements.This article is part of the theme issue ‘Perceptual consciousness and cognitive access’.

Yearly citation counts for the publication titled "A statistical designing approach to MATLAB based functions for the ECG signal preprocessing".

This dataset contains MATLAB scripts created during the work on "Design of experiments: a statistical tool for PIV uncertainty quantification". The proposed UQ approach is applied to estimate the uncertainties in time-averaged velocity and Reynold normal stresses in planar PIV measurements of the flow over a NACA0012 airfoil. The approach is also used to the investigation by stereoscopic PIV of the flow at the outlet of a ducted Boundary Layer Ingesting (BLI) propulsor. The codes in this dataset are used for these two experimental cases.

Matlab source code for generating Figure 1;Matlab source code for generating Figure 2;Matlab source code called by both other Matlab scripts above

corrMatrix.m: This Matlab function computes the correlation matrix of w-test statistics.

KMC.m: This Matlab function computes the critical values for max-w test statistic based on Monte Carlo method. It is needed to run corrMatrix.m befor use it.

kNN.m: This Matlab function based on neural networks allows anyone to obtain the desired critical value with good control of type I error. In that case, you need to download file SBPNN.mat and save it in your folder. It is needed to run corrMatrix.m befor use it.

SBPNN.mat: MATLAB's flexible network object type (called SBPNN.mat) that allows anyone to obtain the desired critical value with good control of type I error.

Examples.txt: File containing examples of both design and covariance matrices in adjustment problems of geodetic networks.

rawMC.txt: Monte-Carlo-based critical values for the following signifiance levels: α′= 0.001, α′= 0.01, α′= 0.05, α′= 0.1 and α′= 0.5. The number of the observations (n) were fixed for each α ′with n = 5 to n= 100 by a increment of 5. For each "n" the correlation between the w-tests (ρwi,wj) were also fixed from ρwi,wj = 0.00 to ρwi,wj = 1.00, by increment of 0.1, considering also taking into account the correlation ρwi,wj = 0.999. For each combination of α′,"n" and ρwi,wj, m= 5,000,000 Monte Carlo experiments were run.

Supplementary material for the manuscript "A Test to Compare Interval Time Series". This includes figures and tables referred to in the manuscript as well as details of scripts and data files used for the simulation studies and the application. All scripts are in MATLAB (.m) format and data files are is MATLAB (.mat) and in EXCEL (. xlsx) formats.

Matlab script to plot figure 3

This resource contains some MATLAB scripts used to teach statistical hydrology section of Physical Hydrology class at the University of Washington, Seattle, WA.