**Visualization of High-Throughput Data on Large-Scale Biological Networks** (**RNaviCell**)

Provides a set of functions to access a data visualization web service. For more information and a tutorial on how to use it, see https://…/nav_web_service.html and https://…/RNaviCell.

**Covariance Matrix Estimation** (**covmat**)

We implement a collection of techniques for estimating covariance matrices. Covariance matrices can be built using missing data. Stambaugh Estimation and FMMC methods can be used to construct such matrices. Covariance matrices can be built by denoising or shrinking the eigenvalues of a sample covariance matrix. Such techniques work by exploiting the tools in Random Matrix Theory to analyse the distribution of eigenvalues. Covariance matrices can also be built assuming that data has many underlying regimes. Each regime is allowed to follow a Dynamic Conditional Correlation model. Robust covariance matrices can be constructed by multivariate cleaning and smoothing of noisy data.

**Feature Selection and Classification with the Embedded Validation Procedures for Biomedical Data Analysis** (**Biocomb**)

Contains functions for the data analysis with the emphasis on biological data, including several algorithms for feature ranking, feature selection, classification algorithms with the embedded validation procedures. The functions can deal with numerical as well as with nominal features. Includes also the functions for calculation of feature AUC (Area Under the ROC Curve) and HUM (hypervolume under manifold) values and construction 2D- and 3D- ROC curves. Biocomb provides the calculation of Area Above the RCC (AAC) values and construction of Relative Cost Curves (RCC) to estimate the classifier performance under unequal misclassification costs problem. Biocomb has the special function to deal with missing values, including different imputing schemes.

**Scale Bar, North Arrow, and Pretty Margins in R** (**prettymapr**)

Automates the process of creating a scale bar and north arrow in any package that uses base graphics to plot in R. Bounding box tools help find and manipulate extents. Finally, there is a function to automate the process of setting margins, plotting the map, scalebar, and north arrow, and resetting graphic parameters upon completion.

**Create a Simple Web API for your R Functions** (**jug**)

A set of convenience functions to build simple APIs.

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