# englelab v1.1.0

Updated: 12 August, 2022

• Major update

• Added new functions to assist in processing and cleaning data.

• center() to calculate z-score or centered scores for a list of variables

• composiste() to calculate a composite score for a group of variables. Composites can be calculated using different methods:

• mean, sum, factor analysis, or principal components
• replace_outliers() to replace outlier values with missing or some other method (e.g., mean). There is an option to save the outlier values to a data file to store a log of what rows in the data were removed.

• remove_missing() to remove subjects with too much missing data on a group of tasks for any given construct.

• Added some extra columns to the output of raw_symspan(), raw_rotspan(), and raw_ospan()

• MemoryTargets and Recalled to more easily see and analyze the sequence of memory items and the sequence of recalled items on each trial.

• EditDistance.unit and EditDistance.load calculate the trial scores based on the edit distance scoring method. See Gonthier et al. (2022).

• Got rid of Recall.correct as it was redundant with Partial.load

• Added extra columns to the output of score_symspan(), score_rotspan(), and score_ospan()

• EditDistanceScore, EditDistanceUnit, and EditDistanceLoad for scores based on the edit distance scoring method. See Gonthier et al. (2022).

Gonthier, C. (2022). An easy way to improve scoring of memory span tasks: The edit distance, beyond “correct recall in the correct serial position.” Behavior Research Methods, 16. https://doi.org/10.3758/s13428-022-01908-2

# englelab v1.0.2

Updated: 5 May, 2022

• Minor update only

• Updated score_visualarrays(). Added a taskname = argument for different types of visual arrays tasks

# englelab v1.0.1

Updated: 12 May, 2021

• Minor update only

• Adds PartialScore and AbsoluteScore variables to score_ functions output for complex-span tasks

# englelab v1.0.0

Updated: 24 February, 2021

• This is a major update and will break previous versions (my apologies)

• This is the first major release and will hopefully be stable after this

• The raw_ functions work pretty much the same and might not break from previous versions

• The score_ functions will break and have been made to be more flexible, such as using them to calculate alternate span scores and reliability estimates.

• The score_ functions need to be used with dplyr::group_by() and so can be thought of as similar to dplyr::summarise()

• See new Vignettes explaining this all: