Found 6 packages in 0.03 seconds
Sparse Interval Sliced Inverse Regression
An interval fusion procedure for functional data in the semiparametric framework of SIR. Standard ridge and sparse SIR are also included in the package.
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)
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
Automatic Statistical Identification in Complex Spectra
With a set of pure metabolite spectra, ASICS quantifies
metabolites concentration in a complex spectrum. The identification of
metabolites is performed by fitting a mixture model to the spectra of the
library with a sparse penalty. The method and its statistical properties are
described in Tardivel et al. (2017)
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. Functions to assess and display important variables are also provided in the package.
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 Chapter 4 of Alia Dehman (2015) < https://hal.archives-ouvertes.fr/tel-01288568v1>.