R

HiCDOC normalizes intrachromosomal Hi-C matrices, uses unsupervised learning to predict A/B compartments from multiple replicates, and detects significant compartment changes between experiment conditions. It provides a collection of functions assembled into a pipeline to filter and normalize the data, predict the compartments and visualize the results. It accepts several type of data: tabular .tsv files, Cooler .cool or .mcool files, Juicer .hic files or HiC-Pro .matrix and .bed files.

SISIR is an R package that implements interval fusion and selection procedures for regression with functional inputs. Models include a semiparametric approach based on Sliced Inverse Regression (SIR) and a random forest based approach. A typical use case of SISIR is the search for time intervals in weather time series, during which the value of the weather impacts the most the yield of fields.

GPGame est un paquet dédié à la recherche d’équilibre de jeux (Nash, etc.) par optimisation bayésienne.

Kernel-based methods are powerful methods for integrating heterogeneous types of data. mixKernel provides methods to combine multiple kernels for unsupervised exploratory analysis or to select features in a kernel for unsupervised analysis or kernel-output prediction. For the multiple kernel problem, different solutions have been implemented to obtain a consensus kernel or a kernel that best preserves the original topology of the data. mixKernel also integrates kernel PCA to visualize similarities between samples in a nonlinear space and from the multiple source point of view. Functions to assess and display important variables using permutations are also provided in the package.