Classification of Spatial Patterns from Environmental Data Through GRadient RECognition (grec)
Provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.

Data to Illustrate OOMPA Algorithms (oompaData)
This is a data-only package to provide example data for other packages that are part of the ‘Object-Oriented Microrray and Proteomics Analysis’ suite of packages. These are described in more detail at the package URL.

Access to Facebook API V2 via a Set of S4 Classes (facebook.S4)
Provides an interface to the Facebook API and builds collections of elements that reflects the graph architecture of Facebook. See <https://…/graph-api> for more information.

Class Unions, Matrix Operations, and Color Schemes for OOMPA (oompaBase)
Provides the class unions that must be preloaded in order for the basic tools in the OOMPA (Object-Oriented Microarray and Proteomics Analysis) project to be defined and loaded. It also includes vectorized operations for row-by-row means, variances, and t-tests. Finally, it provides new color schemes. Details on the packages in the OOMPA project can be found at <http://…/>.

Fitting and Predicting Large-Scale Nonlinear Regression Problems using Multi-Resolution Functional ANOVA (MRFA) Approach (MRFA)
Performs the MRFA approach proposed by Sung et al. (unpublished) to fit and predict nonlinear regression problems, particularly for large-scale and high-dimensional problems. The application includes deterministic or stochastic computer experiments, spatial datasets, and so on.