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.