Version 1.1 of pavo is currently working its way through the internals of CRAN, and should be available over the next day or two. It mostly fixes some bugs following the 1.0 release, and also rolls the segment analysis into the new
colspace() framework, with a new plot of its own.
- segspace() replaces the deprecated segclass(), and is accessed via the colspace() argument space = ‘segment’. The results of segspace() are also now compatible with coldist() for the estimation of Euclidean colour-distances.
- segplot() is a plot for Endler’s (1990) segment analysis, and is accessed — along with all other 2d plots — via plot.colspace()
MINOR FEATURES AND BUG FIXES:
- the use of relative quantum catches is now optional in the categorical colorspace (though still produces a warning), for greater flexibility
- updated several functions to work when rspec object has only one spectrum
- fixed bug in voloverlap where interactive plots would result in error
- fixed incorrect labels in the maxwell triangle plot
- fixed a bug in as.rspec() in which lim was not applied when interpolate = FALSE
- fixed bug in aggplot() which resulted in error when using lty, lwd arguments
- warning if ocular media is being used in both vismodel() and sensmodel()
- added an ‘all’ option to the achromatic argument in vismodel()
- added the ability to calculate dL for cielab models in coldist()
- added some more informative messages and warnings
As always, feel free to get in touch with any issues or suggestions.
We’re running some experiments to understand human visual perception in noisy, real-world settings, and if you happen to be reside around Macquarie University we’d appreciate your help.
What you’d do: Stroll along a 100-m forest track and tell us which coloured model objects you spy. Ideally once on a sunny and once on a cloudy day. All involvement is completely anonymous.
Where: Macquarie University campus near the fauna park (between E8A and W19)
When: Between 11am and 2pm on 1 – 2 weekdays in the first half of May, with flexibility as to your regular daily schedule.
Your reward: Some lunchtime exercise, a warm-fuzzy feeling from helping push back the bounds of science, and probably chocolate. Once you’ve finished strolling, we’d love to share all details of the experiment, what the science predicted and why, etc. You can choose to receive any ultimate study outcomes (reports/papers).
Please email DK if you’re able to help.