Minor changes to theme = 1 in pirateplot(). Changed default value of cap.beans to TRUE
Added color mixing arguments mix.col and mix.p to piratepal(). These allow you to mix the default palettes with a specified color (e.g.; "white")
Added the option to specify data in pirateplot() as a list of numeric vectors, or as a numeric dataframe or matrix without specifying a formula. Each column / element will be taken as a new group.
New palettes in piratepal(): decision.
Fixed bug in sortx in pirateplot(). Sorting data by functions (e.g. sortx = "mean") should now work.
Added gl argument to pirateplot() to specify locations of gridlines (e.g.; gl = seq(0, 10, 1))
Added cex.names argument to control size of bean names (currently this was controlled by cex.lab, which now controls the size of the axis names.)
Some minor changes to default plotting parameters that I think make the default plots look a bit nicer.
Added cap.beans argument to pirateplot(). When cap.beans = TRUE, beans will be cut at the maximum and minimum values of the data.
Added cap.beans argument to pirateplot(). When cap.beans = TRUE, beans will be cut at the maximum and minimum values of the data.
Added two new inf.method values: sd for standard deviation, and se for standard error
Minor updates to themes. Added theme = 3
You can now assign a pirateplot to a variable to return summary statistics.
Added a NEWS.md file to track changes to the package.
Re-structured code generating colors and opacities in pirateplot() to make future updates easier.
Added quant, quant.length and quant.width arguments that add horizontal lines for specified quantiles to each bean (thanks @pat-s)
Added several new arguments (e.g.; bean.fill.col for customising pirateplots
Improved theme support (now under theme rather than theme.o)
pirateplot() can now handle up to 3 IVs!pirateplot(age ~ sex + headband + favorite.pirate, data = pirates).Minor and Bug-fixes
inf.p parameter in pirateplot() was prevously not being passed to the Bayesian HDIs, rendering all inference bands to be the default of 95% (thanks to Roman Pahl for catching this). This has now been fixed.hdi.band argument to pirateplot(). Setting hdi.band = "tight" will constrain inference bands to bean densities.gl.col.