After some delay, we are happy to finally get version 1.1 of the likert package on CRAN. Although labeled 1.1, this is actually the first version of the package released to CRAN. After receiving some wonderful feedback from useR! this year, we held back releasing until we implemented many of the feature suggestions. The NEWS file details most of what is in this release, but here are some highlights:

• Simplify analyzing and visualizing Likert type items using R’s familiar print, summary, and plot functions.
• Create LaTeX and HTML formatted tables using the xtable package. See the likert-xtable vignette for sample output.

There are four demos available:

• likert – Shows most of the features of the package using data from the Programme of International Student Assessment (PISA).
• PreSummarized – Currently only in the development version Shows how to use the likert function with pre-summarized data.
• RecodeFactors – Shows how to recode factors. Useful for setting up your data before calling likert.
• UnusedLevels – This demo shows how to address the “All items (columns) must have the same number of levels” error that occurs when the items passed to likert do not have the same number of levels.

The useR! 2013 slides can be downloaded from Github as well as the abstract. More documentation is available at http://jason.bryer.org/likert and be sure to request feature requests or bug reports on the Github page at https://github.com/jbryer/likert

install.packages('likert',repos='http://cran.r-project.org')
require(likert)

The package is hosted on Github at http://github.com/jbryer/likert. You can always download the latest development version using devtools.

require(devtools)
install_github('likert','jbryer')

We have already started work on version 1.2 which will include:

• The ability to work with pre-summarized data (already on Github, see the summary parameter on the likert function and demo(PreSummarized) for more details).
• A Shiny App to explore the features of the likert package and to work with your own data.
• A more complete vignette.