Estimation of Order of Mixture Distributions

A Mixed-Effects Model for Analyzing Cluster-Level Non-Ignorable
Missing Data
Contains functions for estimating a mixed-effects model for
clustered data (or batch-processed data) with cluster-level (or batch-
level) missing values in the outcome, i.e., the outcomes of some
clusters are either all observed or missing altogether. The model is
developed for analyzing incomplete data from labeling-based quantitative
proteomics experiments but is not limited to this type of data.
We used an expectation conditional maximization (ECM) algorithm for model
estimation. The cluster-level missingness may depend on the average
value of the outcome in the cluster (missing not at random).

Genetics and Independence Testing of Mixed Genetic Panels
Developed to deal with multi-locus genotype data, this package is especially designed for those panel which include different type of markers. Basic genetic parameters like allele frequency, genotype frequency, heterozygosity and Hardy-Weinberg test of mixed genetic data can be obtained.     In addition, a new test for mutual independence which is compatible for mixed genetic data is developed in this package.

stats (>= 3.3),utils (>= 3.2.3), stringr, ggplot2

stats (>= 3.3), utils(>= 3.6.2),ggplot2, data.table, dplyr

stats (>= 3.3), utils(>= 3.6.1), data.table

Omics Data Integration Using Kernel Methods
phyloseq, corrplot, psych, quadprog, LDRTools
c(person("Jerome", "Mariette", role = c("aut", "cre"), email="jerome.mariette@inra.fr"),
person("Nathalie", "Villa-Vialaneix", role = c("aut"), email="nathalie.villa-vialaneix@inra.fr"))
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.