MathWorks - Statistics Toolbox
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The Statistics Toolbox is an easy-to-use environment for analyzing historical data, modeling systems to predict their behavior, developing statistical algorithms, and learning and teaching statistics. Interactive GUI tools let you apply statistical methods easily and consistently, while the MATLAB language lets you easily create custom statistical methods and analyses. This combination gives you the freedom to access functions such as probability and ANOVA directly from the command line, or to use the interactive interfaces to learn and experiment with the Toolbox's built-in visualization and analysis tools.
Updated Jun 12, 2006
by Patrick Rees
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| Statistics M-files
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User contributed statistics m-files from the MATLAB Central File Exchange.
Submitted Apr 18, 2000
Updated Aug 16, 2006
by Chunfa Wu
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| MATLAB, Statistics, and Linear Regression
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Introduction to MATLAB for Statistics. Includes some material on Variance and Covariance, and Linear regression.
Submitted Aug 12, 2004
Updated Aug 16, 2004
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| Advanced Statistics Toolbox
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Includes: Chi lack of fit test, Joint Confidence Region, Least Squares, Least Squares for Response Surfaces, Multivariable Response Regression and Eigenvalues, NonLinear Param estimation, Rational Approx to any data set.
Submitted Jul 19, 1999
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| Statbox Toolbox
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A toolbox of statistical routines, including ordinal logistic regression, Poisson regression, nonlinear regression with sums of exponentials, maximum likelihood and REML for randomized block models, probability distributions, Gaussian quadrature and some other associated special functions and matrix operations.
Submitted Jun 30, 1999
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Total Visits: 2333
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| Statistical Computation: Archives of MATLAB Functions
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Archives on MATLAB functions. Packages include, regresssions, LAD, logit, factor analysis, distributions, statistics, strings/IO, date/time, and utility. Note that functions are minimally documented.
Submitted Aug 12, 2004
Updated Aug 16, 2004
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Total Visits: 1647
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| Stat/Transfer - The Easiest Way to Move Data In and Out of MATLAB
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Stat/Transfer provides a seamless connection between MATLAB and leading statistical packages such as SAS, SPSS, Stata, S-Plus, Gauss, and LIMDEP, databases, such as Access, dBASE, and Paradox, and spreadsheets such as Excel, Quattro, and 1-2-3. It also intelliently handles delimited ASCII data. Available for 32-bit Windows, Linux, and popular Unix platforms.
Submitted Aug 22, 2000
Updated May 03, 2004
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| Statistics Utilities by Peter J.Acklam
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Statistical functions, probability distributions, random numbers. Functions and examples.
Submitted Aug 12, 2004
Updated Aug 16, 2004
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| Clustering package
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A collection of m-files to do clustering. For now, only k-means clustering is implemented and a very slow agglomerative procedure. However, the support routines are quite useful.
Submitted Jun 30, 1999
Updated Mar 28, 2006
by Dong
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| glmlab
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glmlab is a free MATLAB toolbox for analysing generalized linear models. glmlab can fit all types of generalized linear models, including (among others): multiple regression; log-linear models; logistic regression; and weighted regression. To achieve these tasks, it incorporates five error distributions and eight links function--plus the ability to add your own. It uses a graphical user interface and is very easy to learn, and even comes with an on-line manual! Some error analysis is also included, as well as a quick tutorial.
Submitted Aug 31, 1999
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| Kernel Density Estimation Toolbox
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A MATLAB class with MEX routines for creating and manipulating non-parametric (kernel-based) density estimates. Supports quadratic, Gaussian and Laplacian (product) kernels of arbitrary dimension, several automatic bandwith selection methods, and uses KD-tree representations to enable fast approximate evaluation.
Submitted Dec 10, 2003
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| Stixbox: A Statistics Toolbox
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Stixbox, developed by Anders Holtsberg, is a statistics toolbox for MATLAB, Octave, and Matcom/Mideva.
Submitted Feb 22, 2000
Updated Aug 16, 2006
by knowyou
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(13 Ratings)
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Total Visits: 1129
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| Resampling Stats in MATLAB - Software for computer-intensive statistical resampling methods
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Resampling Stats in MATLAB is designed specifically for the "new
statistics" of resampling including bootstrapping and permutation procedures. Resampling methods have become the "treatment of first choice" for confidence intervals, hypothesis testing, and assessing errors in estimates. Resampling Stats in MATLAB gives you the additional commands you need to do resampling easily in MATLAB, with numerous examples. It includes documentation on all resampling commands and pointers on using existing MATLAB commands in your resampling work.
Submitted Apr 07, 2000
Updated May 03, 2004
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| Stochastic simulation using MATLAB
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A tutorial on stochastic simulation. The page demonstrates basic techniques for effective simulation and visualization of a number of random variables and random processes. From the table of contents:
* random numbers from simple distributions;
* basic random processes: random walks,
Poisson processes;
* queuing systems, birth-and-death processes, branching processes;
* counting processes, renewal processes,
renewal reward processes, on-off processes;
* random trees, Waxman random network topology generator, branching Brownian motion in the plane;
* aggregated teletraffic models: superposition of renewal processes, infinite source Poisson (M/G/Infinity) model, integrated sum of on-off processes
Submitted Jan 08, 2006
by Ingemar Kaj Raimundas Gaigalas
Updated Jan 09, 2006
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Total Visits: 1003
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| K Means Clustering Tutorial
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Simple tutorial on what is k means clustering, how the algorithm works, and numerical example of this code (in Matlab)and other resources in k means clustering
Submitted Jul 06, 2005
by Kardi Teknomo
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(21 Ratings)
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Total Visits: 879
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| Spatial Statistics toolbox
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Contains procedures for quickly finding neighboring observations and for speedy estimation of several types of spatial autoregressions (e.g., SAR, CAR). A particular strength of the toolbox is its ability to handle large data sets. Traditionally, the need to evaluate the determinant of a matrix of order n made spatial statistics difficult to implement. Using sparse matrix capabilities and other techniques allows data sets of over 100,000 observations easily.
Submitted Jun 30, 1999
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| MLR with Fit and CV Statistics
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The function mlr.m generates an MLR model fit and does 'leave one out' cross-validation of the model. Measures of R-squared, Adjusted R-squared, root-mean-square error of calibration (RMSEC), and root-mean-square error of cross-validation (RMSECV) are printed to the screen. The function also generates a table with the true and fitted values are along with the relative percent error (RPE) and upper and lower limits of an approximate 95% confidence interval on future observed values assuming normality.
Submitted Nov 02, 1999
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| Mixmod : cluster analysis and discriminant analysis sofware
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The MIXMOD (MIXture MODelling) software fits mixture models to a given data set with a density estimation, a clustering or a discriminant analysis purpose.
A large variety of algorithms to estimate the mixture parameters are proposed (EM, Classification EM, Stochastic EM) and it is possible to combine them to lead to different strategies in order to get a sensible maximum of the likelihood (or complete-data likelihood) function.
Moreover, different information criteria for choosing a parsimonious model (the number of mixture components, for instance), some of them favoring either a cluster analysis or a discriminant analysis view point, are included.
Written in C++, MIXMOD is interfaced with MATLAB.
2006-06-22 : Mixmod 1.7.1 is available
2007-02-06 : Mixmod 2.0 is available :
this new major release includes the treatment of qualitative data
see : http://www-math.univ-fcomte.fr/mixmod/news.php
Submitted Nov 29, 2005
by Florent
Updated Feb 06, 2007
by Florent
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Total Visits: 671
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| MCMC Methods for MLP and GP and Stuff
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MCMCstuff toolbox is a collection of Matlab functions for Bayesian inference with Markov chain Monte Carlo (MCMC) methods. Includes code and demonstrations for neural networks and Gaussian processes for classification and regression.
Submitted Sep 30, 2006
by Aki Vehtari
Updated Oct 02, 2006
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| Correlation Pseudocolor Map Function
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The function corrmap.m displays a pseudocolor map of the correlation matrix for a input data set. This much would be easy, but it can also reorder the variables so that they are grouped by how correlated they are with each other. A modified k-nearest neighbor algorithm is used to reorder the variables. An example of its use is shown below. Here the data file plsdata was loaded into the MATLAB workspace. The plsdata file contains data from a slurry-fed ceramic melter process for solidifying the reprocessing wastes from nuclear fuels (yes, I know that this example is a bit unusual).
Submitted Nov 02, 1999
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| Resources for K-Mean Clustering
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Aside from my tutorial (in Visual Basic Code or in MATLAB code), there are many books and journals or Internet resources discuss about K-mean clustering, your search must be depending on your application. Below are a few list that you may consider.
Submitted Jun 15, 2005
by MATLAB Central Admin
Updated Feb 21, 2006
by Bhavana
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| Tutorials on Processing of Data using MATLAB
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Tutorials below aimed at freshman engineering students using Student Version 7.01 of MATLAB, i.e. with only the symbolic toolbox.
Submitted Jun 03, 2005
by William R. Wilcox
Updated Jun 06, 2005
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| GML RANSAC Matlab Toolbox
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GML RANSAC Matlab Toolbox addresses the problem of parametric model estimation.
Toolbox is a set of MATLAB scripts, implementing RANSAC algorithm family, for robust estimation:
*RANSAC
*LMedS
*LO-MSAC
*MLESAC
*MSAC
*NAPSAC
*R-RANSAC
*ZHANGSAC
Submitted Apr 03, 2006
by Alexander Vezhnevets
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| Estimation of Distribution Algorithms
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MATEDA implements a number of Estimation of Distribution Algorithms (EDAs) commonly found in the literature. These programs are conceived to help in the initial validation of EDA approaches to different optimization problems. Current implementation includes EDAs for discrete and continuous problems. EDAs based on Bayesian and undirected graphical models have been included.
Submitted Oct 13, 2005
by Roberto Santana
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| chsone.m
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Chi-Square Significance Test.
Submitted Jul 19, 1999
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| MANI: Manifold Learning Demo
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This Matlab GUI provides a simple interface for visualizing and experimenting with various dimensionality reduction and manifold learning techniques. It is intended as an instruction tool and has proven useful to researchers interested in learning about dimensionality reduction. The algorithms available include: Principal Components Analysis (PCA), Multi-Dimensional Scaling (MDS), ISOMAP, Locally Linear Embedding (LLE), Hessian eigenmaps (HLLE), Laplacian eigenmaps, Diffusion maps, and Local Tangent Space Alignment (LTSA). The GUI provides several simple datasets such as the Swiss Roll for experimentation and comparison. Users can import their own datasets from text files or from the workspace.
Submitted Jun 02, 2006
by Todd Wittman
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| Spatial Statistics Toolbox for MATLAB 1.1
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Spatial Statistics Toolbox for MATLAB 1.1 includes code for simultaneous spatial autoregressions (SAR), conditional spatial autoregressions (CAR), and mixed regressive spatially autoregressive (MRSA) models, along with some additions.
Submitted Apr 28, 2000
Updated May 04, 2004
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| mad.m
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Median Absolute Distances from the sample median. Y = MAD(X) computes the robust estimator of scale MAD (Median Absolute Distances from the sample median) for the vector X.
Submitted Aug 13, 1999
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| Projection methods
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These programs use the techniques described in Ken Judd's 1992 "Journal of Economic Theory" article to solve the standard growth model using parameterized expectations. Another good reference for the solution methods used in these programs is the working paper "Algorithms for Solving Dynamic Models with Occasionally Binding Constraints" by Larry Christiano and Jonas Fisher.
Submitted Jun 30, 1999
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| Data Description Toolbox (dd_tools)
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The data description toolbox wants to provide tools, classifiers and evaluation functions for the research of one-class classification (or data description)
Submitted Sep 15, 2005
by M.Alipour
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| 50-50 MANOVA with rotation testing
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The Matlab function ffmanova.m performs general linear modeling with multiple responses (MANCOVA). An overall p-value for each model term is calculated by the 50-50 MANOVA method, which handles collinear responses. Rotation testing is used to compute adjusted single response p-values according to familywise error rates and false discovery rates. Predictions, mean predictions and least squares means can also be calculated.
Submitted Aug 21, 2000
Updated Oct 27, 2006
by Øyvind Langsrud
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| Galton's Triangle experiment
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This is a graphical simulation of the famous Galton's triangle: a ball falls and meets nails arranged according to a triangular shape. Each time the ball meets a nail, it bounces to the right or the the left of it, according to a probability p=0.5, and independently of the previous stage.
Submitted Jul 19, 1999
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| wmedian.m
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The function wmedian(data) computes the Hodges-Lehmann estimator W for the population central tendency.
Submitted Jul 19, 1999
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| RMTool: Random Matrix Calculator
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RMTool is a simple MATLAB Symbolic Toolbox based code for analytically predicting the eigenvalue distribution of a large class of complicated random matrices.
Submitted Feb 16, 2006
by N. Raj Rao
Updated Feb 17, 2006
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| ARMADA Data Mining Tool
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An association rule data mining tool for experimentation and analysis. The associated files can be downloaded at the MATLAB Central File Exchange.
Submitted Feb 13, 2003
Updated Feb 23, 2005
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Total Visits: 324
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| Data Mining in MATLAB
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Exploring data mining using MATLAB (and sometimes MATLAB Toolboxes).
Submitted Jul 19, 2007
by Will Dwinnell
Updated Aug 02, 2007
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Total Visits: 225
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| Spatial Analysis 3D
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Spatial Analysis 3D is a user-friendly, graphical user interface (GUI) that allows statistical and visual manipulations of real and simulated three-dimensional spatial point patterns. Examples of the types of analyses performed include those derived from the Delaunay tessellation associated with such spatial point patterns, and those associated with the correlation of such point patterns, including autocorrelation analysis and its derived density recovery profile, as well as the related K, F, and G-functions. The stimulus for the development of Spatial Analysis 3D has been the study of neuronal positioning within the central nervous system, but many other applications in science, engineering, statistics and mathematics should benefit from this suite of programs. Spatial Analysis 3D is the project of a collaborative research effort between Drs. Benjamin Reese, Mary Raven, and Dan Lofgreen at the Unversity of California at Santa Barbara and Dr. Stephen Eglen at the University of Cambridge. It has been supported by a grant from the National Institute of Mental Health through the Neurotechnology Research, Development and Enhancement Program. It grew out of our efforts to quantify the regularity and simulate the patterning found in distributions of nerve cells across the retina, a structure in the central nervous system where uniformity in nerve cell spacing plays a critical role in retinal function.
Submitted Nov 09, 2007
by Mary Raven
Updated Nov 12, 2007
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| RanLip - universal nonuniform multivariate random variate generator
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RanLip is a method of generation of random variates with arbitrary Lipschitz-continuous densities, which works in the univariate and multivariate cases, in up to 5-6 variables. A Matlab toolbox is available, which includes the manual, examples and mex file.
Submitted Dec 19, 2006
by Gleb Beliakov
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Total Visits: 174
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