Changes in 0.1.7: * Compatible with dplyr > 0.5.0.
* Fixes issue described in https://github.com/jwdink/eyetrackingR/issues/57 * Fixes bug in add_aoi when only one AOI is added.
Changes in 0.1.6: * Allows for treatment-coded variables in
lmer time-bin or cluster analysis, via the “treatment_level” argument.
Changes in 0.1.5: * Fixes compatibility issue with latest version of
Changes in 0.1.4: * A variety of important bug-fixes for onset-contingent analysis. The rest of the package is unchanged.
Changes in 0.1.3:
analyze_time_binsand therefore cluster-analyses have been re-written internally. Full support for (g)lm, (g)lmer, wilcox. Support for interaction terms/predictors. Experimental support for using boot-splines within cluster analysis.
analyze_time_binsand cluster analyses
analyze_boot_splinesare now deprecated. To perform this type of analysis, use
analyze_time_clustersfunction now checks that the extra arguments passed to it are the same as the arguments passed
simulate_eyetrackingr_datafunction to generate fake data for simulations.
Changes in 0.1.1:
clean_by_trackloss. Previously did not work for certain column names.
make_eyetrackingr_data. Previously did not work correctly with
treat_non_aoi_as_missing = TRUE.
analyze_time_clusters: previously did not compute permutation-distribution correctly.
make_time_sequence_datato summarize. This DV can then be plotted and used in downstream functions (like
analyze_time_binsand functions that call this (e.g
analyze_time_clusters, allowing the user to take advantage of multiple cores to speed up this relatively slow function.
get_time_clustersfor getting information about clusters in a data.frame (rather than a printed summary– better for programming).