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Gaussian process (GP) models are commonly used statistical metamodels for emulating expensive computer simulators. Fitting a GP model can be numerically unstable if any pair of design points in the in...
With the increasing availability of public cloud computing facilities and scientific super computers, there is a great potential for making R available through public or shared resources. This allows ...
PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vect...
abc: an R package for Approximate Bayesian Computation (ABC)     abc  package  Approximate Bayesian Computation       font style='font-size:12px;'> 2011/7/7
Many recent statistical applications involve inference under complex models, where it is computationally prohibitive to calculate likelihoods but possible to simulate data.
In multivariate nonparametric analysis, sparseness of the co- variates also called curse of dimensionality, forces one to use large smoothing parameters. This leads to a biased smoother. Instead of ...
For statistical analysis of functional Magnetic Resonance Imaging (fMRI) data sets, we propose a data-driven approach based on Independent Component Analysis (ICA) implemented in a new version of the ...
This paper describes an R package implementing large sample tests and confidence intervals (based on the central limit theorem) for various parameters. The one and two sample mean and variance conte...

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