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Examples using 'RcppClassic' to Interface R and C++
The 'Rcpp' package contains a C++ library that facilitates the integration of R and C++ in various ways via a rich API. This API was preceded by an earlier version which has been deprecated since 2010 (but is still supported to provide backwards compatibility in the package 'RcppClassic'). This package 'RcppClassicExamples' provides usage examples for the older, deprecated API. There is also a corresponding package 'RcppExamples' with examples for the newer, current API which we strongly recommend as the basis for all new development.
Omics Data Integration Project
Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.
Spatial Point Patterns Analysis
Perform first- and second-order multi-scale analyses derived from Ripley K-function, for univariate, multivariate and marked mapped data in rectangular, circular or irregular shaped sampling windows, with tests of statistical significance based on Monte Carlo simulations.
Using GPUs in Statistical Genomics
Can be used to carry out permutation resampling inference in the context of RNA microarray studies.
Wavelet Leaders in Multifractal Analysis
Analyzing the texture of an image from a multifractal wavelet leader analysis.
Detect Differentiation Problems
An algorithm based on graph theory tools to detect differentiation problems. A differentiation problem occurs when aggregated data are disseminated according to two different nomenclatures. By making the difference for an additive variable X between an aggregate composed of categories of the first nomenclature and an other aggregate, included in that first aggregate, composed of categories of the second nomenclature, it is sometimes possible to derive X on a small aggregate of records which could then lead to a break of confidentiality. The purpose of this package is to detect the set of aggregates composed of categories of the first nomenclature which lead to a differentiation problem, when given a confidentiality threshold.
Chordalysis R Package
Learning the structure of graphical models from datasets with thousands of variables. More information about the research papers detailing the theory behind Chordalysis is available at < http://www.francois-petitjean.com/Research> (KDD 2016, SDM 2015, ICDM 2014, ICDM 2013). The R package development site is < https://github.com/HerrmannM/Monash-ChoR>.
Marine Regions Data from 'Marineregions.org'
Tools to get marine regions data from < http://www.marineregions.org/>. Includes tools to get region metadata, as well as data in 'GeoJSON' format, as well as Shape files. Use cases include using data downstream to visualize 'geospatial' data by marine region, mapping variation among different regions, and more.
Analyses of Phylogenetic Treeshape
Simulation and analysis
of phylogenetic tree topologies using statistical indices. It
is a companion library of the 'ape' package. It provides
additional functions for reading, plotting, manipulating
phylogenetic trees. It also offers convenient web-access to
public databases, and enables testing null models of
macroevolution using corrected test statistics. Trees of class
"phylo" (from 'ape' package) can be converted easily.
Implements methods described in Bortolussi et al. (2005)