eyetrackingR

Changes in 0.2.0 * Repairing for resubmission to CRAN * Adding compatibility with new versions of dplyr, tidyr and ggplot2 * Inbuilt support for binomial, glmmPQL and glmmTMB models

Changes in 0.1.8: * Fixes a bug in make_onset_data.

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 lm or lmer time-bin or cluster analysis, via the “treatment_level” argument.

Changes in 0.1.5: * Fixes compatibility issue with latest version of lme4 package.

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:

• The analyze_time_bins and 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.
• P-value adjustment for multiple comparisons is now supported in analyze_time_bins
• Easier to use AOI as a predictor/covariate in analyze_time_bins and cluster analyses
• The functions make_boot_splines_data and analyze_boot_splines are now deprecated. To perform this type of analysis, use test="boot_splines" in analyze_time_bins.
• Warnings and errors are now given in the returned dataframe for analyze_time_bins.
• Fixed plotting methods for time-cluster data
• The analyze_time_clusters function now checks that the extra arguments passed to it are the same as the arguments passed
• Fixed small bug in make_onset_data
• Added simulate_eyetrackingr_data function to generate fake data for simulations.

Changes in 0.1.1:

• Important bug-fix in clean_by_trackloss. Previously did not work for certain column names.
• Important bug-fix in make_eyetrackingr_data. Previously did not work correctly with treat_non_aoi_as_missing = TRUE.
• Important bug-fix in analyze_time_clusters: previously did not compute permutation-distribution correctly.
• Can specify any arbitrary dependent-variable for make_time_window_data or make_time_sequence_data to summarize. This DV can then be plotted and used in downstream functions (like analyze_time_bins or make_time_cluster_data)
• Bug-fix in error/warning reporting in analyze_time_bins and functions that call this (e.g make_time_cluster_data).
• Compatible with ggplot2 2.0
• Small bug fix in cluster analyses functions related to the dots (…) arguments
• Added support for parallelization in analyze_time_clusters, allowing the user to take advantage of multiple cores to speed up this relatively slow function.
• Added get_time_clusters for getting information about clusters in a data.frame (rather than a printed summary– better for programming).
• Small bug-fixes in make-boot-splines.
• Changed how cluster-summaries are displayed