Debugging in R with restore points (restorepoint)
The package restorepoint allows to debug R functions via restore points instead of break points. When called inside a function, a restore point stores all local variables. These can be restored for later debugging purposes by simply copy & pasting the body of the function from the source code editor to the R console. This vignette briefly illustrates the use of restore points and compares advantages and drawbacks compared to the traditional method of setting break points via browser(). Restore points are particularly convenient when using an IDE like RStudio that allows to quickly run selected code from a script in the R Console.

Creating R exercises with automatic assement of student’s solutions (RTutor)
RTutor is an R package that allows to develop interactive R exercises. Problem sets can be solved off-line or can hosted in the web via shiny server. Problem sets can be designed as a Markdown .rmd file (to be solved directly in RStudio) or use a browser-based interface powered by RStudio’s Shiny.

Discriminant Non-Negative Matrix Factorization (DNMF)
Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. This algorithm refers to an article, Zafeiriou, Stefanos, et al. “Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification.” Neural Networks, IEEE Transactions on 17.3 (2006): 683-695.

Fitting Step-Functions (stepR)
Allows to fit step-functions to univariate serial data where neither the number of jumps nor their positions is known.

Semiparametric Estimation of Stochastic Frontier Models (semsfa)
Semiparametric Estimation of Stochastic Frontier Models following a two step procedure: in the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions of the functional form representing technology and in the second step variance parameters are obtained by pseudolikelihood estimators or by method of moments.

Community Structure Detection via Modularity Maximization (modMax)
The algorithms implemented here are used to detect the community structure of a network. These algorithms follow different approaches, but are all based on the concept of modularity maximization.

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