PascalKieslich/mousetrap: mousetrap v3.1.0

Announcements A first paper on the mousetrap software packages has been accepted for publication. It presents the mousetrap plugin for creating mouse-tracking experiments in OpenSesame and also includes a short demonstration of basic analyses using the mousetrap R package. Reference: Kieslich, P. J., & Henninger, F. (in press). Mousetrap: An integrated, open-source mouse-tracking package. Behavior Research Methods. doi:10.3758/s13428-017-0900-z Changes in specific functions mt_derivatives: now always reports acceleration as difference in absolute velocity (the argument acc_on_abs_vel has been removed). Besides, the argument absolute has been introduced that indicates if absolute values for distances and velocities should be reported (by default, this is not the case). All of this is only relevant if a single dimension is specified in dimensions. mt_sample_entropy: the default values reported have changed (cf. bug fix below). mt_sample_entropy now only uses a custom function for computing sample entropy (which is faster and produces virtually identical results as pracma::sample_entropy if the same parameters are used). Therefore, the method argument has been removed. Besides, the lag argument has been renamed to m. mt_distmat, mt_cluster, mt_cluster_k, and mt_map: now provide the option to remove trajectory points containing missing values (by setting the na_rm argument to TRUE). Removal is done column-wise. That is, if any trajectory has a missing value at, e.g., the 10th recorded position, the 10th position is removed for all trajectories. mt_distmat, mt_cluster, mt_cluster_k, and mt_map: now allow for specifying the relative importance of each trajectory dimension via the weights argument. Technically, each variable is rescaled so that the standard deviation matches the corresponding value in weights. By default, weights is a vector of 1s implying equal importance of each dimension (i.e., all dimensions are standardized to a standard deviation of 1). This changes the default behavior of the functions compared to the previous release where the original variables were used without standardization. To use the original variables, set weights = NULL. mt_map: uses mousetrap::mt_prototypes as default for prototypes if no prototypes are provided. mt_average: now internally replaces NaNs with NAs. NaNs only occur if a specific dimension contains only NAs for an interval (which in practice only happens for acc values if the trial stops at the interval boundary). mt_standardize: now by default standardizes mouse-tracking measures across all trials if no within variable is specified. Bugs fixed mt_sample_entropy: Bug fixed for method="pracma" (the default method): The window size argument (which used to be specified using the lag argument - now this has been renamed to m) was incorrectly passed on to the tau argument of pracma::sample_entropy. It should have beend passed on to the edim argument. After fixing this, both methods in mt_sample_entropy provided virtually identical results (which is why the method argument has been dropped, see above). New functions bezier: create Bezier-curves using the Bernstein approximation. mt_scale_trajectories: standardize variables in a mouse trajectory array. mt_heatmap_raw: create high-resolution heatmap of trajectory data. mt_heatmap: plot trajectory heatmap using base plots. mt_heatmap_ggplot: plot trajectory heatmap using ggplot. mt_diffmap: create a difference-heatmap of two trajectory heatmap images. mt_animate: create a gif trajectory animation. Please note that although these functions have been tested extensively, they still have beta status. New data KH2017_raw: Raw mouse-tracking dataset from Kieslich & Henninger (in press). KH2017: Mouse-tracking dataset from Kieslich & Henninger (in press)