Omics Data Integration Using Kernel Methods (mixKernel)
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.

Classification of Functional Data (classiFunc)
Efficient implementation of k-nearest neighbor estimator and a kernel estimator for functional data classification.

Excess Mass Calculation and Plots (ExcessMass)
Implementation of a function which calculates the empirical excess mass for given \eqn{\lambda} and given maximal number of modes (excessm()). Offering powerful plot features to visualize empirical excess mass (exmplot()). This includes the possibility of drawing several plots (with different maximal number of modes / cut off values) in a single graph.

Multiple Imputation by Chained Equations with Multilevel Data (micemd)
Addons for the ‘mice’ package to perform multiple imputation using chained equations with two-level data. Includes imputation methods specifically handling sporadically and systematically missing values. Imputation of continuous, binary or count variables are available. Following the recommendations of Audigier, V. et al (2017), the choice of the imputation method for each variable can be facilitated by a default choice tuned according to the structure of the incomplete dataset. Allows parallel calculation for ‘mice’.

Death Registration Coverage Estimation (DDM)
A set of three two-census methods to the estimate the degree of death registration coverage for a population. Implemented methods include the Generalized Growth Balance method (GGB), the Synthetic Extinct Generation method (SEG), and a hybrid of the two, GGB-SEG. Each method offers automatic estimation, but users may also specify exact parameters or use a graphical interface to guess parameters in the traditional way if desired.

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