Package: mixEMM
Title: A Mixed-Effects Model for Analyzing Cluster-Level Non-Ignorable
Missing Data
Version: 1.0
Date: 2017-06-06
Author: Lin S. Chen, Pei Wang, and Jiebiao Wang
Maintainer: Lin S. Chen <lchen@health.bsd.uchicago.edu>
Description: 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).
License: GPL
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2017-06-08 03:09:44 UTC; JWang
Repository: CRAN
Date/Publication: 2017-06-08 15:21:36 UTC

Title: Genetics and Independence Testing of Mixed Genetic Panels
Imports: stats (>= 3.3),utils (>= 3.2.3), stringr
Description: 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.

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

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

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

Package: mixKernel
Type: Package
Title: Omics Data Integration Using Kernel Methods
Version: 0.1
Date: 2017-05-17
Depends: R (>= 2.10), mixOmics, ggplot2
Imports: phyloseq, corrplot, psych, quadprog, LDRTools
Author: 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"))
Maintainer: Jerome Mariette <jerome.mariette@inra.fr>
Description: 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.
License: GPL (>= 2)
Repository: CRAN
Date/Publication: 2017-05-18 03:50:57 UTC
Packaged: 2017-05-17 08:00:43 UTC; jmariette
NeedsCompilation: no
LazyData: true