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)
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