R packages (maintainer)


SOMbrero is an R package that implements stochastic self-organizing maps (SOM) variants for numeric and non numeric datasets.

Role in the project: Maintainer and author
Project home page on CRAN
Current version: 1.2-4 (March 2019)
Install SOMbrero with
SOMbrero comes with a Web User Interface (using shiny) that can be used by typing the command line:

Archives (package source and Windows built prior version 1.0)

If you are using SOMbrero, please cite:
  • Olteanu M., Villa-Vialaneix N. (2015) On-line relational and multiple relational SOM. Neurocomputing, 147, 15-30.
  • Mariette J., Rossi F., Olteanu M., Villa-Vialaneix N. (2016) Accelerating stochastic kernel SOM. XXVth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017), Verleysen M. (ed), i6doc, Bruges, Belgium, 269-274.


SISIR is an R package that implements the sparse interval sliced inverse regression. It also provides an implementation of standard ridge and sparse SIR for high dimensional data

Role in the project: Maintainer and author
Project home page on CRAN
Current version: 0.1 (Dec 2016)
Install SISIR with

If you are using SISIR, please cite:
  • Picheny V., Servien R., Villa-Vialaneix N. (2016) Interpretable sparse SIR for digitized functional data. Preprint.


RNAseqNet is an R package that implements network inference with log-linear Poisson Graphical Model. Hot-deck multiple imputation method can be used to improve the reliability of the inference with an auxiliary dataset.

Role in the project: Maintainer and author
Project home page on CRAN
Current version: 0.1.2 (January 2018)
Install RNAseqNet with

If you are using RNAseqNet, please cite:
  • Imbert, A., Valsesia, A., Le Gall, C., Armenise, C., Gourraud, P.A., Viguerie, N. and Villa-Vialaneix, N. (2017) Multiple hot-deck imputation for network inference from RNA sequencing data. Bioinformatics, 34(10), 1726-1732.

R packages (author)


mixKernel is an R package that provides methods to combine kernels for unsupervised exploratory analysis. Different solutions are implemented to compute a meta-kernel, in a consensus way or in a way that best preserves the original topology of the data. mixKernel also integrates kernel PCA to visualize similarities between samples in a non linear space and from the multiple source point of view. Functions to assess and display important variables are also provided in the package.

Role in the project: Author (Maintainer is Jérôme Mariette)
Project home page on CRAN
Current version: 0.3 (November 2018)
Install mixKernel with

mixKernel is soon to be part of mixOmics: check our tutorial.
If you are using mixKernel, please cite:
  • Mariette, J., Villa-Vialaneix, N. (2017) Unsupervised multiple kernel learning for heterogeneous data integration. Bioinformatics, 34(6), 1009-1015.


ASICS is an R package that quantifies metabolites concentration in a complex spectrum using a set of pure metabolite spectra. The identification of metabolites is performed by fitting a mixture model to the spectra of the library with a sparse penalty.

Role in the project: Author (Maintainer is Gaëlle Lefort)
Project home page on Bioconductor
Current version: 2.2.0 (May 2019)
Install ASICS with
if (!requireNamespace("BiocManager", quietly = TRUE))
BiocManager::install("ASICS", version = "3.8")

If you are using ASICS, please cite:
  • Lefort G., Liaubet L., Canlet C., Tardivel P., Père M.C., Quesnel H., Paris A., Iannuccelli N., Vialaneix N., Servien R. (2019) ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra. Bioinformatics, 35(21): 4356-4363.
  • Tardivel P., Canlet C., Lefort G., Tremblay-Franco M., Debrauwer L., Concordet D., Servien R. (2017). ASICS: an automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra. Metabolomics, 13(10): 109.


adjclust is an R package that implements a constrained version of hierarchical agglomerative clustering, in which each observation is associated to a position, and only adjacent clusters can be merged. Typical application fields in bioinformatics include Genome-Wide Association Studies or Hi-C data analysis, where the similarity between items is a decreasing function of their genomic distance. Taking advantage of this feature, the implemented algorithm is time and memory efficient.
adjclust has been partially developed as a 2017 GSoC project.

Role in the project: Author (Maintainer is Pierre Neuvial)
Project home page on CRAN
Current version: 0.5.0 (December 2019)
Install adjclust with

If you are using adjclust, please cite:
  • Dehman A. (2015) Spatial Clustering of Linkage Disequilibrium blocks for Genome-Wide Association Studies. PhD thesis, Université d'Évry.

R packages (unofficial)


NiLeDAM is an R package for dating monazites from measurements of triplets (U,Th,Pb) obtained by a microprobe technique. The implemented method is the one described in the article (Montel et al., 1996) and the package has been developed from the original programs made in basic language by Jean-Marc Montel (École Nationale Supérieure de Géologie, France). An example, kindly provided by Anne-Magali Seydoux-Guillaume is included in the package and is the one described in the article (Seydoux-Guillaume et al., 2012). Not maintained anymore
Monazite image by Aangelo (personal work) - licence GFDL or CC-BY-SA-3.0-2.5-2.0-1.0, via Wikimedia Commons

Role in the project: Maintainer and author
Project home page on R-Forge
Current version: 0.1-alpha (Aug. 2013)
Install NiLeDAM with
 install.packages("NiLeDAM", repos="") 
(the package nleqslv must have been previously installed from CRAN).
A Web User Interface (using shiny) can be used directly on-line for those who are not familiar with R programming language. It is available at The script of the web user interface is provided on GitHub:
			git clone

If you are using NiLeDAM, please cite:
  • Villa-Vialaneix N., Montel J.M., Seydoux-Guillaume A.M. (2013) NiLeDAM: Monazite Datation for the NiLeDAM team. R package version 0.1.
  • Montel J.M., Foret S., Veschambre M., Nicollet C., Provost A. (1996) Electron microprobe dating of monazite. Chemical Geology, 131, 37-53.
  • Seydoux-Guillaume A.M., Montel J.M., Bingen B., Bosse V., de Parseval P., Paquette J.L., Janots E., Wirth R. (2012) Low-temperature alteration of monazite: fluid mediated coupled dissolution-precipitation, irradiation damage and disturbance of the U-Pb and Th-Pb chronometers. Chemical Geology, 330-331, 140-158.
Cited in:
  • Laurent A.T., Seydoux-Guillaume, A.M., Duchene S., Bingen B., Bosse V., Datas L. (2016) Sulphate incorporation in monazite lattice and dating the cycle of sulphur in metamorphic belts. Contributions to Mineralogy and Petrology, 171, 94.

And you can also check my github repository for other contributions, including a use case example for SISIR and a comparison of random forest strategies for big data in R.