Changed conjugate prior of Normal/LogNormal distributions to be the

`NormalInverseGamma`

distribution from a combination of the`Normal`

and`Inverse Gamma`

distributions. This distribution is bivariate and gives us a 2d estimate for both`x`

and`sig_sq`

. The params for this distribution are`mu`

,`lambda`

,`alpha`

,`beta`

and are different from the old priors that Normal/LogNormal were expecting.Various doc changes to illustrate these changes and new expectations

- Fix closed form distributions and added tests
- Calculation Posterior Expected Loss is now correct and represents a true loss function
- Added
`plotNormalInvGamma`

- Colors for sample plots are now hardcoded (red for > 0 and blue for < 0)
- Plots are truncated at the extremes to avoid very long tails

Added

`grab`

and`rename`

to retrieve and rename posteriors from your`bayesTest`

objectMostly useful in conjunction with

`combine`

in order to quickly chain together several`bayesTest`

s- Correctly hide legend for generic plots
Standardized prior parameters to have the same arguments as the

`plot{Dist}`

functions- This mostly changes prior inputs for
`bayesTest(distribution = c('normal', 'lognormal'))`

This is a

**breaking**change

- Moved
`distribution`

metadata from`bayesTest$distribution`

to`bayesTest$inputs$distribution`

to be consistent - Reconcile posterior names to always be
`A`

and`B`

and not include the parameter name `A_data`

and`B_data`

in inputs are now always lists by default to make`combine`

work more simply- Big refactor of how
`bayesTest`

works internally. Dispatch per distribution is now only related to how the posterior is calculated. - Some error checking has been made more generic

- Posterior Expected Loss now correctly displays 0 instead of NaN for that case
- Numerous doc/examples/tests cleanup
- Overall refactor of some methods, making it easier to read and contribute

- added
`banditize`

and`deployBandit`

to turn your`bayesTest`

object into a Bayesian multi*armed bandit and deploy as a JSON API respectively. Added programmatic capabilities on top of existing interactive uses for

`plot`

generic functionYou can now assign

`plot(bayesTestObj)`

to a variable and not have it automatically plot.- Added quantile summary of calculated posteriors to the output of
`summary.bayesTest`

Added Posterior Expected Loss to output of

`summary.bayesTest`

- This is useful to know when to stop your Bayesian AB Test
Supports the risk of choosing ‘B’ over ‘A’ (ordering is important) and makes more sense if A > B currently in the test

outputs from

`plot`

generics are now explicitly`ggplot`

objects and can be modified as suchYou can input your own titles/axis labels/etc if the defaults don’t fit your use case

- First major CRAN release
- 6 (+ 2) distributions
`print`

,`plot`

,`summary`

generics- Easy plotting of distributions for quick visual inspection
`combine`

tests as needed- 100% code coverage