bestNormalize 1.9.1.9000

• Started testing lambert type transforms again.
• adding lambert of type “hh” as option when allow_lambert_h is set to TRUE, per request from issue 24
• fixing deprecation of dplyr::progress_estimated (#26)

bestNormalize 1.9.1

• New function, bestLogConstant, that uses the same machinery to pick the best value of a constant to use when logging a variable, e.g. the one that makes the distribution look the most normal, especially useful for non-positive or zero-inflated data. Currently experimental.
• Taking out tests that failed due to dependent package update (does not impact default bestNormalize behavior). See (issue)[https://github.com/gmgeorg/LambertW/issues/3].

bestNormalize 1.9.0

• Add S3 methods that helps step_orderNorm() to work with parallel processing.
• Add S3 methods that helps step_best_normalize() to work with parallel processing.
• Add a new transformation: the double reversed log (@rempsyc #18)
• Fix issues in CRAN checks

bestNormalize 1.8.3

• updating print functionality to remain compatible with recipes.
• updated term selection machinery to remain compatible with recipes.

bestNormalize 1.8.2

• improving scalability of boxcox in response to issue 10; thank you to Krzysztof Dyba (kadyb) for the suggestions.
• improved scalability of yeojohnson, thanks to Emil Hvitfeldt (EmilHvitfeldt) for his work on this problem for the recipes package here.
• updated tests to remain compatible with new recipes package (>0.1.16)

bestNormalize 1.8.1

• update citation (new R Journal publication!)
• fix/add features to tidy method to work more generally, provide easy access to chosen transformations (responding to issue 9)

bestNormalize 1.8.0

• added packagedown website here: https://petersonr.github.io/bestNormalize
• Implemented GH actions (code coverage and R CMD check) via usethis in response to issue 7
• Improved scalability of ORQ transformation via n_logit_fit argument, with default of 10000. This should substantially decrease memory use of orderNorm while only minimally affecting the out-of-domain approximations.
• Updated documentation

bestNormalize 1.7.0

• changed step_bestNormalize to step_best_normalize, responding to 8
• Fixed error in documentation regarding LambertW transformation types (thank you to Georg M. Goerg, the author of LambertW, for pointing this out).
• Add center_scale transform as default when standardize == TRUE
• Added error when trying to use repeated CV with much too small of folds
• Changed a few T and F to TRUE and FALSE
• Added documentation of how one can use scales and ggplot2 to visualize all transformations.
• Added butcher and axe functionality in order to improve scalability of step_* functions
• Improved tidy functionality with bestNormalize and step_best_normalize

bestNormalize 1.6.1

• Fixed bug that was causing simple transforms to fail in bestNormalize
• Updated to new LambertW version in dependencies (request from CRAN)

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

bestNormalize 1.5.0

• 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.

bestNormalize 1.4.3

• 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

bestNormalize 1.4.2

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

bestNormalize 1.4.1

• 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.

bestNormalize 1.3.0

• 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

bestNormalize 1.2.0

• 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.

bestNormalize 1.1.0

• 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

bestNormalize 1.0.1

• 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

bestNormalize 1.0.0

• 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

bestNormalize 0.2.2

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

bestNormalize 0.2.1

• Made description more clear in response to comments from CRAN

bestNormalize 0.2.0

First submission to CRAN