Mixmod is a well-established software package for fitting mixture models of multivariate Gaussian or multinomial probability distribution functions to a given dataset with either a clustering, a density estimation or a discriminant analysis purpose. The Rmixmod S4 package provides an interface from the R statistical computing environment to the C++ core library of Mixmod (mixmodLib). In this article, we give an overview of the model-based clustering and classification methods implemented, and we show how the R package Rmixmod can be used for clustering and discriminant analysis.
Simultaneous clustering of rows and columns, usually designated by bi-clustering, coclustering or block clustering, is an important technique in two way data analysis. A new standard and efficient approach has been recently proposed based on the latent block model (Govaert and Nadif 2003) which takes into account the block clustering problem on both the individual and variable sets. This article presents our R package blockcluster for co-clustering of binary, contingency and continuous data based on these very models. In this document, we will give a brief review of the model-based block clustering methods, and we will show how the R package blockcluster can be used for co-clustering.
Let Y be an Ornstein-Uhlenbeck diffusion governed by a stationary and ergodic process X. We give condition for ergodicity of Y and give conditions that ensures existence of moment for the invariant law of Y
During the past decade, a useful model for nonstationary random elds has been developed. This consists of reducing the random eld of interest to isotropy via a bijective bi-continuousdeformation of the index space. Then the problem consists of estimating this space deformation together with the isotropic correlation in the deformed index space. We propose to estimate both this space deformation and this isotropic correlation using a constrained continuousversion of the simulated annealing for a Metropolis-Hastingsdynamic. This method providesa nonparametricestimation of the deformationwhich has the required property to be bijective; so far, the previous nonparametric methods do not guarantee this property. We illustrate our work with two examples, one concerninga precipitationdataset. We also give one idea of how spatial prediction should proceed in the new coordinate space.
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