SOM Bound to Realize Euclidean and Relational Outputs

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) , Olteanu & Villa-Vialaneix (2005) and Cottrell et al (2004) . 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'.

Log-Linear Poisson Graphical Model with Hot-Deck Multiple Imputation

Infer log-linear Poisson Graphical Model with an auxiliary data set. Hot-deck multiple imputation method is used to improve the reliability of the inference with an auxiliary dataset. Standard log-linear Poisson graphical model can also be used for the inference and the Stability Approach for Regularization Selection (StARS) is implemented to drive the selection of the regularization parameter. The method is fully described in .

Select Intervals Suited for Functional Regression

Interval fusion and selection procedures for regression with functional inputs. Methods include a semiparametric approach based on Sliced Inverse Regression (SIR), as described in (standard ridge and sparse SIR are also included in the package) and a random forest based approach, as described in .

Monazite Dating for the NiLeDAM Team

Th-U-Pb electron microprobe age dating of monazite, as originally described in .

Omics Data Integration Using Kernel Methods

Kernel-based methods are powerful methods for integrating heterogeneous types of data. mixKernel aims at providing methods to combine kernel for unsupervised exploratory analysis. Different solutions are provided 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 . A method to select (as well as funtions to display) important variables is also provided .

Testing Differences Between Families of Trees

Perform test to detect differences in structure between families of trees. The method is based on cophenetic distances and aggregated Student's tests.

Adjacency-Constrained Clustering of a Block-Diagonal Similarity Matrix

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. This algorithm is described in Ambroise et al (2019) .

Pathways Longitudinal and Differential Analysis in Metabolomics

Perform a differential analysis at pathway level based on metabolite quantifications and information on pathway metabolite composition. The method is based on a Principal Component Analysis step and on a linear mixed model. Automatic query of metabolic pathways is also implemented.

Inverse Probability of Censoring Weights to Deal with Treatment Switch in Randomized Clinical Trials

Contains functions for formatting clinical trials data and implementing inverse probability of censoring weights to handle treatment switches when estimating causal treatment effect in randomized clinical trials.

Weighted Cox-Regression for Nested Case-Control Data

Fit Cox proportional hazard models with a weighted partial likelihood. It handles one or multiple endpoints, additional matching and makes it possible to reuse controls for other endpoints Stoer NC and Samuelsen SO (2016) .