**Implicit Association Test Scores Using Robust Statistics** (**IATscores**)

Compute several variations of the Implicit Association Test (IAT) scores, including the D scores (Greenwald, Nosek, Banaji, 2003) and the new scores that were developed using robust statistics (Richetin, Costantini, Perugini, and Schonbrodt, 2015).

**Quantitative Support of Decision Making under Uncertainty** (**decisionSupport**)

Supporting the quantitative analysis of binary welfare based decision making processes using Monte Carlo simulations. Decision support is given on two levels: (i) The actual decision level is to choose between two alternatives under probabilistic uncertainty. This package calculates the optimal decision based on maximizing expected welfare. (ii) The meta decision level is to allocate resources to reduce the uncertainty in the underlying decision problem, i.e to increase the current information to improve the actual decision making process. This problem is dealt with using the Value of Information Analysis. The Expected Value of Information for arbitrary prospective estimates can be calculated as well as Individual and Clustered Expected Value of Perfect Information. The probabilistic calculations are done via Monte Carlo simulations. This Monte Carlo functionality can be used on its own.

**Check the Clustering Tendency** (**clustertend**)

Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins’ statistic is implemented.

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