implement plot.ebnm, summary.ebnm, print.summary.ebnm

[willwerscheid]

For plot, maybe do x vs. posterior$mean (would need to return x and s in a data frame called data). @pcarbo 's suggestions for summary:

• sample size
• prior type
• scale (fixed or estimated)
• quantiles of input data, posterior means, and lfsr.
• g (fixed or estimated)

[willwerscheid]

For plot, can also use plot_diagnostic from ashr.

[pcarbo]

@willwerscheid Take a look at the varbvs package to get started on implementing these functions.

[willwerscheid]

The plotting code for the wOBA example from my thesis should work