1 Start-up notes

1.1 Instalation

The splithalf package can be installed from CRAN or Github:

install.packages("splithalf")
# or the development version
# devtools::install_github("sdparsons/splithalf")

also note that the package requires the following; tidyr, dplyr, Rcpp, robustbase. You can load them with the following

library("tidyr")
library("dplyr")
library("Rcpp")
library("robustbase")

1.2 Version note

The current version of splithalf is 0.5.2 [unofficial version name: “Fight Milk - rebrand”]

A huge part of developing this latest version has been condensing the many splithalf functions into a single one. In addition to this, I have adapted the underlying code using the amazing Rcpp package (Eddelbuettel et al. 2018), enabling me to input some C++ code. While my C++ code is rudimentary, the splithalf function now runs around 20x faster than before.

1.3 Citing the package

Citing packages is one way for developers to gain some recognition for the time spent maintaining the work. I would like to keep track of how the package is used so that I can solicit feedback and improve the package more generally. This would also help me track the uptake of reporting measurement reliability over time.

Please use the following reference for the code: Parsons, S. (2017). splithalf: robust estimates of split half reliability (Version 4). figshare. https://doi.org/10.6084/m9.figshare.5559175.v4

If (eventually) this documentation turns into a publication, this reference will change.

1.4 User feedback

Developing the splithalf package is a labour of love (and, occasionally, burning hatred). If you have any suggestions for improvement, additional functionality, or anything else, please contact me or raise an issue on github. Likewise, if you are having trouble using the package (e.g. scream fits at your computer screen – we’ve all been there) do contact me and I will do my best to help as quickly as possible. These kind of help requests are super welcome. In fact, the package has seen several increases in performance and usability due to people asking for help.

1.5 A note on terminology used in this document

It is important that we have a similar understanding of the terminology I use in the package and documentation. Each is also discussed in reference to the functions later in the documentation.

  • Trial – whatever happens in this task, e.g. a stimuli is presented. Importantly, participants give one response per trial
  • Trial type – often trials can be split into different trial types (e.g. to compare congruent and incongruent trials)
  • Condition - this might be different blocks of trials, or something to be assessed separately within the functions. e.g. a task might have a block of ‘positive’ trials and a block of ‘negative’ trials.
  • Datatype - I use this to refer to the outcome of interest. specifically whether one is interested in average response times or accuracy rates
  • Score - I use score to indicate how the final outcome is measured; e.g. the average RT, or the difference between two average RTs, or even the difference between two differences between two RTs (yes, the final one is confusing)

References

Eddelbuettel, Dirk, Romain Francois, JJ Allaire, Kevin Ushey, Qiang Kou, Nathan Russell, Douglas Bates, and John Chambers. 2018. Rcpp: Seamless R and C++ Integration. https://CRAN.R-project.org/package=Rcpp.