PascalKieslich/mousetrap: mousetrap 3.1.3

Announcements A book chapter on the mousetrap software packages has been published. It covers many common analyses using the mousetrap R package. Please cite it as follows when using mousetrap in your research: Kieslich, P. J., Henninger, F., Wulff, D. U., Haslbeck, J. M. B., & Schulte-Mecklenbeck, M. (2019). Mouse-tracking: A practical guide to implementation and analysis. In M. Schulte-Mecklenbeck, A. Kühberger, & J. G. Johnson (Eds.), A Handbook of Process Tracing Methods (pp. 111-130). New York, NY: Routledge. Besides, if you use functions for clustering and mapping trajectories, please also include the following reference: Wulff, D. U., Haslbeck, J. M. B., Kieslich, P. J., Henninger, F., & Schulte-Mecklenbeck, M. (2019). Mouse-tracking: Detecting types in movement trajectories. In M. Schulte-Mecklenbeck, A. Kühberger, & J. G. Johnson (Eds.), A Handbook of Process Tracing Methods (pp. 131-145). New York, NY: Routledge. Changes in specific functions mt_sample_entropy: By default, sample entropy is calculated based on the differences of the position values (following Hehman et al., 2015). An optional argument use_diff now has been introduced to allow users to override this behavior and use the untransformed values instead, by setting use_diff=FALSE. mt_align_start_end: Now checks, if start and end points are equal for a trial (separately per dimension). If so, returns a warning message as the aligned trajectory values for the respective dimension will be NaN/Inf/-Inf