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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. Jerome Mariette and
Nathalie Villa-Vialaneix (2018)
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
Sparse Interval Sliced Inverse Regression
An interval fusion procedure for functional data in the
semiparametric framework of SIR, as described in
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) < https://almob.biomedcentral.com/articles/10.1186/s13015-019-0157-4>.
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.
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.
Inference about the standardized mortality ratio when evaluating the effect of a screening program on survival.
The InferenceSMR package provides functions to make inference about the standardized mortality ratio (SMR) when evaluating the effect of a screening program. The package is based on methods described in Sasieni (2003) and Talbot et al. (2011).
Construct Consensus Genetic Maps and Estimate Recombination Rates
Construct consensus genetic maps with LPmerge, see Endelman and Plomion (2014)
Spatial Oblique Decision Tree
SPODT is a spatial partitioning method based on oblique decision trees, in order to classify study area into zones of different risks, determining their boundaries