M

MIAT software

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

The R package treediff performs Hi-C data differential analysis based on pixel-level differential analysis and a post hoc inference strategy to quantify signal in clusters of pixels. Clusters of pixels are obtained through a connectivity-constrained two-dimensional hierarchical clustering.

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.

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

It performs Hi-C data differential analysis based on pixel-level differential analysis and a post hoc inference strategy to quantify signal in clusters of pixels. Clusters of pixels are obtained through a connectivity-constrained two-dimensional hierarchical clustering.

mixKernel is a multiple kernel framework that allows to integrate multiple datasets of various types into a single analysis. The package is published on CRAN: https://cran.r-project.org/package=mixKernel

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

The stochastic (also called on-line) version of the Self-Organising Map (SOM) algorithm is provided. Different versions of the algorithm are implemented, for numeric and relational data and for contingency tables as described, respectively, in Kohonen (2001) isbn:3-540-67921-9, Olteanu & Villa-Vialaneix (2005) doi:10.1016/j.neucom.2013.11.047 and Cottrell et al (2004) doi:10.1016/j.neunet.2004.07.010. The package also contains many plotting features (to help the user interpret the results), can handle (and impute) missing values and is delivered with a graphical user interface based on 'shiny'.