Risk Management (riskR)
Computes risk measures from data, as well as performs risk management procedures such as practical risk measurement, capital requirement, capital allocation and decision-making.
Clustering of Variables Around Latent Variables (ClustVarLV)
The clustering of variables is a strategy for deciphering the underlying structure of a data set. Adopting an exploratory data analysis point of view, the Clustering of Variables around Latent Variables (CLV) approach has been proposed by Vigneau and Qannari (2003). Based on a family of optimization criteria, the CLV approach is adaptable to many situations. In particular, constraints may be introduced in order to take account of additional information about the observations and/or the variables. In this paper, the CLV method is depicted and the R package ClustVarLV including a set of functions developed so far within this framework is introduced. Considering successively different types of situations, the underlying CLV criteria are detailed and the various functions of the package are illustrated using real case studies.
Assertions to Check Types of Variables (assertive.types)
A set of predicates and assertions for checking the types of variables. This is mainly for use by other package developers who want to include run-time testing features in their own packages. End-users will usually want to use assertive directly.
Software Hotspot Analysis (hotspot)
Contains data for software hotspot analysis, along with a function performing the analysis itself.
Biological Network Construction, Visualization and Analyses (ProNet)
High-throughput experiments are now widely used in biological researches, which improves both the quality and quantity of omics data. Network-based presentation of these data has become a popular way in data analyses. This package mainly provides functions for biological network construction, visualization and analyses. Networks can be constructed either from experimental data or from a set of proteins and integrated PPI database. Based on them, users can perform traditional visualization, along with the subcellular localization based ones for Homo sapiens and Arabidopsis thaliana. Furthermore, analyses including topological statistics, functional module clustering and go profiling can also be achieved.