xLLiM provides tools for non linear regression using mixture of regression models and an inverse regression strategy. The methods include the GLLiM model based on Gaussian mixtures, a robust version of GLLiM, named SLLiM based on a mixture of Generalized Student distributions, and a block version of GLLiM. Web site
FDG (one-Factor copulas with Durante Generators). This R package provides tools for building high-dimensional copulas with the FDG model, a class of multivariate copulas based on one-factor copulas (Mazo et al, 2016). FDG copulas are a class of copulas featuring an interesting balance between flexibility and tractability. This package provides tools to construct, calculate the pairwise dependence coefficients of, simulate from, and fit FDG copulas. The acronym FDG stands for one-Factor with Durante Generators, as an FDG copula is a one-factor copula - that is, the variables are independent given a latent factor - whose linking copulas belong to the Durante class of bivariate copulas (also referred to as exchangeable Marshall-Olkin or semilinear copulas). Web site
MSST (Mixtures of Multiple scaled Student distributions). The package is not yet available on the CRAN. It implements more efficiently the models and inference procedures described in Forbes and Wraith 2014 and has been used on brain MRI data sets (Arnaud et al, 2018). Some subroutines have been written in C++. Web site: no public website yet.
PBC (Product of Bivariate Copulas). This R package is not maintained on the CRAN anymore. It provides tools for building copulas with the PBC model, a class of multivariate copulas based on Products of Bivariate Copulas (Mazo et al 2015). Copulas are a useful tool to model multivariate distributions. While there exist various families of bivariate copulas, much fewer has been done when the dimension is higher. To this aim an interesting class of copulas based on products of transformed copulas has been proposed. However the use of this class for practical high dimensional problems remains challenging. Constraints on the parameters and the product form render inference, and in particular the likelihood computation, difficult. In this R package, we propose a new class of high dimensional copulas based on a product of transformed bivariate copulas. No constraints on the parameters refrain the applicability of the proposed class which is well suited for applications in high dimension. Furthermore the analytic forms of the copulas within this class allow to associate a natural graphical structure which helps to visualize the dependencies and to compute the likelihood efficiently even in high dimension. Web site
fMRImodelFIT (Matlab) Matlab Codes used in the paper [Mesejo et al 2015] (see files in Annex) to perform the estimation of biophysical parameters from fMRI BOLD data. This package also includes the 6 synthetic datasets used in the experiments. The codes has been
registered at APP in 2016 under the CeCILLB license.
PyHRF software This software development project that followed the HEROES project. The goal is to build on our work on fMRI analysis to eventually reach clinical applications. This implies the development of a user-friendly and flexible software. The PyHRF package will
provide a set of tools for addressing the two main issues involved in intra-subject fMRI data analysis: (i) the localization of cerebral regions that elicit evoked activity and (ii) the estimation of the activation dynamics also referenced to as the recovery of the Hemodynamic Response Function (HRF). To tackle these two problems, PyHRF
implement the last research developments of the project partners which are MISTIS and PARIETAL teams, CEA NeuroSpin, GIN and IRIT Toulouse.
A paper in Frontiers in Neuroscience gives more details on the current PyHRF functionalities [Vincent et al 2014].
The P-LOCUS software
The P-LOCUS software addresses the delineation of lesions in pathological brains. It was implemented with
financial support from GRAVIT (Grenoble Alpes Valorisation Innovation Technologies with the goal to create a Start-up.
P-LOCUS analyses a combined dataset of various 3D MRI sequences. Its originality comes from:
1) it is fully automatic: no external user interaction and no training data required; 2)
the possibility to combine information from several images (MR sequences), 3)
a statistical Bayesian framework for robustness to image artefacts and a priori knowledge incorporation;
4) a voxel-based clustering technique that uses Markov random fields (MRF) incorporating information about neighboring voxels for spatial consistency and robustness to imperfect image features (noise);
5) the possibility to select and incorporate relevant a priori knowledge via different atlases, e.g. tissue and vascular territory atlases;
6) a fully integrated preprocessing steps and lesion ROI identification.
The P-LOCUS software was presented at various conferences and used for the BRATS Challenge on tumor segmentation organized as a satellite challenge of the Miccai conference in Nagoya, Japan in 2013. A paper published in IEEE trans. on Medical Imaging
reports the challenge results. P-LOCUS was best on average among unsupervised methods. The software has been registered at APP in 2013 and is now undergoing industrial development within the
Pixyl startup created in March 2015.
Web site .
The SpaCEM3 software The SpaCEM3 (Spatial Clustering with EM and Markov Models) software
replaces and adds new functionalities to
the former still available SEMMS (Spatial EM for Markovian Segmentation) package.
SpaCEM3 proposes a variety of algorithms
for image segmentation, supervised and unsupervised classification of
multidimensional, possibly incomplete and spatially located data.
The main techniques use the EM algorithm for soft clustering and
Markov Random Fields for spatial modelling. The learning and inference
parts are based on recent developments based on mean
field approximations. New additions include:
a) New Markov models including various extensions of the Potts model and
triplets Markov models;
b) Additional treatment of very high dimensional data using dimension
reduction techniques within a classification framework;
c) Models and methods allowing supervised classification with new learning and
test steps. The software is written in C++ (35000 lines) and is
available with a new user interface at
A user guide and example
data sets are also available there. The software has been
registered at APP in 2007 under the CeCILLB license. A description
of its main functionalities are also available in French
[Revue Modulad 2009].
The SEMMS software Spatial EM for Markovian Segmentation. [Link].
The FASTRUCT software (for Windows OS)
clustering programs have gained increased popularity in studies of
population structure since the publication of the software
structure. These programs are generally acknowledged as performing
well, but their running-time may be prohibitive. FASTRUCT is a
non-Bayesian implementation of the classical model with
no-admixture uncorrelated allele frequencies. This new program
relies on the Expectation-Maximization principle, and produces
assignment rivaling other model-based clustering programs. In
addition, it can be several-fold faster than Bayesian
implementations [Chen et al. 2006]. The software consists of a command-line engine,
which is suitable for batch-analysis of data, and a MS Windows
graphical interface, which is convenient for exploring data. It has been registered at APP in 2006,
under the CeCILLB license.
The TESS software TESS is a computer program in C++
that implements our Bayesian clustering
algorithm for spatial population genetics [Chen et al. 2007]. Is it particularly useful for
seeking genetic barriers or genetic discontinuities in continuous
populations. The method is based on a hierarchical mixture model where
the prior distribution on cluster labels is defined as a Hidden Markov
Random Field (Francois et al. 2006). Given individual geographical
locations, the program seeks population structure from multilocus
genotypes without assuming predefined populations. TESS takes input data
files in a format compatible to existing non-spatial Bayesian algorithms
(e.g., STRUCTURE). It returns graphical displays of cluster membership
probabilities and geographical cluster assignments from its Graphical
User Interface. It has been registered at APP in 2006, under the CeCILLB license.
The ECMPR software The ECMPR (Expectation Conditional Maximization Point Registration)
Registration) package implements the technique described in (Dewaele et al, ECCV 2006) and
(Horaud et al, IEEE PAMI 2010). It
registers two (2D or 3D) point clouds using an algorithm based on
maximum likelihood with hidden variables. The method can register
both rigid and articulated shapes. It estimates both the rigid or
the kinematic transformation between the two shapes as well as the
parameters (covariances) associated with the underlying Gaussian
mixture model. The software is written in C++ (2800 lines) and has been registered at APP in April
2010 under the GPL license.
The LOCUS software
The LOCUS software analyses in few minutes a 3D MR brain scan
and identifies brain tissues and a large number of brain
structures. An image is divided into cubes on each of which a
statistical model is applied. This provides a number of local
treatments that are then integrated to ensure consistency at a
global level. It results a low sensitivity to artefacts. The
statistical model is based on a Markovian approach which enables
to capture the relations between tissues and structures, to
integrate a priori anatomical knowledge and to handle local
estimations and spatial correlations (Scherrer et al. MICCAI 2009). A flyer in French and English, a slide,
and a video are available for a short description.
The software is written in C++ (50000 lines).
[Link]. It has been registered at APP in 2010 under an
The POPEYE software POPEYE contains software modules and libraries jointly developed by three partners within the POP STREP project: INRIA, University of Sheffield, and
University of Coimbra. It includes kinematic and dynamic control
of the robot head, stereo calibration, camera-microphone
calibration, auditory and image processing, stereo matching,
binaural localization, audio-visual speaker localization.
Currently this software package is not distributed outside POP.