a Small (Matlab/Octave) Toolbox for Kriging
The STK is a (not so) Small Toolbox for Kriging. Its primary focus in on the interpolation / regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior. The STK also provides tools for the sequential and non-sequential design of experiments. Even though it is, currently, mostly geared towards the Design and Analysis of Computer Experiments (DACE), the STK can be useful for other applications areas (such as Geostatistics, Machine Learning, Non-parametric Regression, etc.).
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. Nevertheless the toolbox capabilities to simulate numerical solutions of SDE systems are still valid and can serve as a useful starting point to those willing to simulate stochastic dynamical models easily.
Implementation of the A2RMS Algorithm in Matlab
Implementation of the A2RMS Algorithm for univariate densities defined for real values.
A Matlab software routine to perform Principal Component Analysis using Covariance, Correlation or Comedian as the criterion. Though, initially developed for experiments related to fretting wear but can be effectively used to interpret experimental data from any field. The attached files contain source code as well as a sample MATLAB (.mat) data file of 13 variables. It could be replaced to the data file of your choice. The code is open source but you are requested to give credits if used. Additionally, it also has some useful functions for exporting and generating publication quality figures for different kind of figures in MATLAB
RIPE (Regulatory network Inference from joint Perturbation and Expression data) is a novel three-step method that integrates both perturbation data and steady state gene expression data in order to estimate a regulatory network. The ripe package is written in R, with additional functionality provided by a MATLAB executable file. The executable file uses a runtime engine called the MATLAB Compiler Runtime (MCR). The executable for different architectures is distributed on this site together with the R package itself.
Provides an API, which enables the Matlab or Octave user (and developer) to incorporate Space Weather data and metadata resources from the Space Physics Interactive Data Resource (SPIDR) directly into an Matlab/Octave application. Examples included.
Tail probability calculator for continuous random variable
A suite of Matlab functions that calculate the tail probability / cdf / pdf / quantile of linear combination of random variables in one of the following classes: (1) symmetric random variables with support on the real axis (normal, Student's t, uniform and triangular); (2) random variables with support on the positive real axis (chi-squared and log-Lambert W x chi-squared distributions; inverse gamma distribution is temporarily disabled due to numerical issues).
VAPoRS stands for "Variable-dimensional Approximate Posterior for Relabeling and Summarization".
approximate Bayesian computation for stochastic differential equations
A MATLAB toolbox for approximate Bayesian computation (ABC) in stochastic differential equation models. It performs approximate Bayesian computation for stochastic models having latent dynamics defined by stochastic differential equations (SDEs) and not limited to the "state-space" modelling framework. Both one- and multi-dimensional SDE systems are supported and partially observed systems are easily accommodated. Variance components for the "measurement error" affecting the data/observations can be estimated. A 50-pages Reference Manual is provided with two case-studies implemented and discussed. The methodology is based on the research article available at http://arxiv.org/abs/1204.5459 Author's research page is http://www.maths.lth.se/matstat/staff/umberto/
A Matlab toolbox for interfacing with the pure JAVA numerical library Snifflib. This toolbox provides convenience m-files for interoperability with Snifflib from within an active Matlab session running a JAVA virtual machine.