bestNormalize 1.6.1

bestNormalize 1.6.0

Added ability to supply user-defined transformations and associated vignette
Added in ability to supply user-defined normalization statistics and (the same) associated vignette
Take out standardize option from no_transform so x.t always matches input vector.
Minor programming improvements

Added step_bestNormalize and step_orderNorm functions for implementation within recipes.
Changed default to warn = FALSE when calling bestNormalize. If a transformation doesn’t work, warnings will no longer be shown by default unless warn is set to TRUE.

Allow options to be passed through bestNormalize to specific transformation functions
Slight bug fix to square root transformation (a = 0 by default, not .001)
Slight bug fix in the “quiet” argument for bestNormalize with LOO
Slight bug fix to plot.bestNormalize which was improperly labeling transformations
exp_x having trouble with standardize option, so added option allow_exp_x to bestNormalize to allow a workaround, and changed it so if any infinite values are produced during the transformation, exp_x will not work (that way, bestNormalize will not include this in its results).
Progress bar will now only displayed if quiet is FALSE and length(x) > 2000

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
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”.