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Projet de la version V2 de l'application des ORE.

Le projet est constitué de 2 sous projet :

La partie serveur qui fournit les web services de l'application

La partie UI qui fournit une interface VueJS permettant d'interroger ces Web Service

R package for regression and discrimination, with special focus on chemometrics and high-dimensional data

The goal of this package is to help users in using Capsis software and compile reports for field workshops. The workshop consist in predicting management effects on forest stand with Samsara2 model within Capsis platform.

http://martelosim.lessem.forge.inrae.fr/

The two libraries of the XpertMass project are libXpertMassCore and libXpertMassGui. The two libraries expose common non-gui and gui functionalities, respectively, that are used by two software pieces of the msXpertSuite organization: MassXpert and MineXpert.

Make brand new polymer chemistry definitions; Use the definitions to perform calculations in a desktop calculator-like manner; Perform sophisticated polymer sequence mass spec simulations ; Compute isotopic clusters from formulas

This project is the source repository of the CRAN package 'RNAseqNet' https://cran.r-project.org/package=RNAseqNet.

This package infers a log-linear Poisson Graphical Model with an auxiliary dataset. Hot-deck multiple imputation method is used to improve the reliability of the inference with an auxiliary dataset DOI:10.1093/bioinformatics/btx819. The package also implements standard log-linear Poisson GM (without missing data) <a href"https://dx.doi.org/10.1109/BIBM.2012.6392619">DOI:10.1109/BIBM.2012.6392619 and the StARS criterion to help with the choice of the regularization parameter.

https://ssnmr.sk8.inrae.fr

R package to run MuSICA

This project is the source repository of the CRAN package 'SISIR' https://cran.r-project.org/package=SISIR.

This package can perform interval fusion and selection procedures in regression models with functional inputs. Implemented methods include a semiparametric approach based on Sliced Inverse Regression (SIR), as described in doi:10.1007/s11222-018-9806-6 (standard ridge and sparse SIR are also included in the package) and a random forest based approach, as described in doi:10.1002/sam.11705.