- Update citation to point to newly published work.
- Update maintainer email to new address (same person, new affiliation).

- Correctly subtract 1/2 from ranks in ORQ transformation to make quantile estimation unbiased (this was a bug in 1.3.0, as ranks start at 1, not zero). Divides by n instead of n+1.
- Specify the weights for the GLM in the ORQ transformation to be the number of observations. This doesn’t change the transformation but seems to have a bit faster computational speed, and it’s more mathematically tractable.
- Other various bug fixes to tests and to plotting functions.

- Add 1/2 to ranks in ORQ transformation to make quantile estimation unbiased (should have minimal impact)
- Add option
`loo`

for leave-one-out cross-validation - Add progress bar for cross-validation methods (both with/without parallel)
- Add “no_transform” function - does the same thing as I(x) but in the syntax of other transformations (this allows the normalization statistics to also be calculated if no transformation is performed).
- Add support for lambert transforms of type “h” in the
`bestNormalize`

function via`allow_lambert_h`

argument. - Add “before standardization” to printout of different transforms’ means and sds to clarify output

- Added other transformations commonly used to normalize a vector
- exponential, log, square root, arcsinh

- Lambert WxF is no longer done by default by bestNormalize since it is unstable on certain OS (Linux, Solaris), and does not abide by the CRAN policy.

- Clarified that the transformations are standardized by default, and providing option to not standardize in transformations
- Updated tests to run a bit faster and to use proper S3 classes

- Added references for original papers (Van der Waerden, Bartlett) that cite the basis for the orderNorm transformation, as well as discussion in Beasley (2009)
- Edited description to clarify that this procedure is a new adaptation of an older technique rather than a new technique in itself

- Added feature to estimate out-of-sample normality statistics in bestNormalize instead of in-sample ones via repeated cross-validation
- Note: set
`out_of_sample = FALSE`

to maintain backward-compatibility with prior versions and set`allow_orderNorm = FALSE`

as well so that it isn’t automatically selected

- Note: set
- Improved extrapolation of the ORQ (orderNorm) method
- Instead of linear extrapolation, it uses binomial (logit-link) model on ranks
- No more issues with Cauchy transformation

Added plotting feature for transformation objects

Cleared up some documentation

- Changed the name of the orderNorm technique to “Ordered Quantile normalization”.

- Made description more clear in response to comments from CRAN